This is the report of the W3C Emotion Incubator Group (EmoXG) as specified in the Deliverables section of its charter.
In this report we present requirements for information that needs to be represented in a general-purpose Emotion Markup Language in order to be usable in a wide range of use cases.
Specifically the report:
The report identifies various areas which require further investigation and debate. The intention is that it forms a major input into a new Incubator Group which would develop a draft specification as a proposal towards a future activity in the W3C Recommendation Track.
This section describes the status of this document at the time of its
publication. Other documents may supersede this document. A list of Final Incubator Group
Reports is available. See also the W3C
technical reports index at http://www.w3.org/TR/.
This document was developed by the W3C Emotion Incubator Group. It represents the consensus view of the group, in particular those listed in the acknowledgements, on requirements for a generally usable emotion markup language. The document has two main purposes:
Publication of this document by W3C as part of the W3C Incubator Activity indicates no endorsement of its content by W3C, nor that W3C has, is, or will be allocating any resources to the issues addressed by it. Participation in Incubator Groups and publication of Incubator Group Reports at the W3C site are benefits of W3C Membership.
Incubator Groups have as a goal to produce work that can be implemented on a Royalty Free basis, as defined in the W3C Patent Policy. Participants in this Incubator Group have made no statements about whether they will offer licenses according to the licensing requirements of the W3C Patent Policy for portions of this Incubator Group Report that are subsequently incorporated in a W3C Recommendation.
Foreword: A Word
2. Scientific Descriptions of Emotion
3. Use Cases
5. Assessment of Existing Markup Languages
6. Summary and Outlook
Appendix 1: Use Cases
Appendix 2: Detailed Assessment of Existing Markup Languages
This document is a report of the W3C Emotion Incubator group, investigating the feasibility of working towards a standard representation of emotions and related states in technological contexts.
This document is not an attempt to "standardise emotions", nor is it an attempt to unify emotion theories into one common representation. The aim is not to understand the "true nature" of emotions, but to attempt a transfer - making available descriptions of emotion-related states in application-oriented technological contexts, inspired by scientific proposals, but not slavishly following them.
At this early stage, the results presented in this document are preliminary; the authors do not claim any fitness of the proposed model for any particular application purpose.
In particular, we expressly recommend prospective users of this technology
to check for any (implicit or explicit) biases, misrepresentations or
omissions of important aspects of their specific application domain. If you
have such observations, please let us know -- your feedback helps us create a
specification that is as generally usable as possible!
The W3C Emotion Incubator group was chartered "to investigate the prospects of defining a general-purpose Emotion annotation and representation language, which should be usable in a large variety of technological contexts where emotions need to be represented".
What could be the use of such a language?
From a practical point of view, the modeling of emotion related states in technical systems can by important for two reasons.
1. To enhance computer-mediated or human-machine communication. Emotions are a basic part of human communication and should therefore be taken into account, e.g. in emotional Chat systems or emphatic voice boxes. This involves specification, analysis and display of emotion related states.
2. To enhance systems' processing efficiency. Emotion and intelligence are
strongly interconnected. The modeling of human emotions in computer
processing can help to build more efficient systems, e.g. using emotional
models for time-critical decision enforcement.
A standardised way to mark up the data needed by such "emotion-oriented systems" has the potential to boost development primarily because
a) data that was annotated in a standardised way can be interchanged between systems more easily, thereby simplifying a market for emotional databases.
b) the standard can be used to ease a market of providers for sub-modules
of emotion processing systems, e.g. a web service for the recognition of
emotion from text, speech or multi-modal input.
The work of the present, initial Emotion Incubator group consisted of two main steps: firstly to revisit carefully the question where such a language would be used (Use cases), and secondly to describe what those use case scenarios require from a language (Requirements). These requirements are compared to the models proposed by current scientific theory of emotions (Scientific descriptions). In addition, existing markup languages are discussed with respect to the requirements (Existing languages).
The specification of an actual emotion markup language has not yet been started, but is planned as future work (Summary and Outlook). This deviation from the original plan was the result of a deliberate choice made by the group - given the strong commitment by many of the group's members to continue work after the first year, precedence was given to the careful execution of the first steps, so as to form a solid basis for the more "applicable" steps that are the logical continuation of the group's work.
Throughout the Incubator Activity, decisions have been taken by consensus during monthly telephone conferences and two face to face meetings.
The following report provides a detailed description of the work carried out and the results achieved so far. It also identifies open issues that will need to be followed up in future work.
The Incubator Group is now seeking to re-charter as an Incubator group for
a second and final year. During that time, the requirements presented here
will be prioritised; a draft specification will be formulated; and possible
uses of that specification in combination with other markup languages will be
outlined. Crucially, that new Incubator group will seek comment from the W3C
MMI and VoiceBrowser working groups. These comments will be decisive for the
decision whether to move into the Recommendation Track.
The group consisted of representatives of 16 institutions from 11 countries in Europe, Asia, and the US:
* Original sponsor organisation
+ Invited expert
It can be seen from this list that the interest has been broad and
international, but somewhat tilted towards the academic world. It will be one
important aim of a follow-up activity to produce sufficiently concrete output
to get more industrial groups actively interested.
One central terminological issue to be cleared first is the semantics of
the term emotion, which has been used
in a broad and a narrow sense.
In its narrow sense, as it is e.g. used by Scherer (2000), the term refers to what is also called a prototypical emotional episode (Russell & Feldman Barrett 1999), full blown emotion, or emergent emotion (Douglas-Cowie et al. 2006): a short, intensive, clearly event triggered emotional burst. A favourite example would be "fear" when encountering a bear in the woods and fleeing in terror.
Especially in technological contexts there is a tendency to use the term emotion(al) in a broad sense, sometimes for almost everything that cannot be captured as purely cognitive aspect of human behaviour. More useful established terms -- though still not concisely defined -- for the whole range of phenomena that make up the elements of emotional life are "emotion-related states" and "affective states".
A number of taxonomies for these affective states have been proposed. Scherer (2000), e.g., distinguishes:
This list was extended / modified by the HUMAINE group working on databases: in Douglas-Cowie et al. (2006) the following list is proposed (and defined):
Emergent emotions -- not without
reason also termed prototypical emotional episodes -- can be viewed as
the archetypical affective states and many emotional theories focus on them.
Empirical studies (Wilhelm, Schoebi & Perrez 2004) on the other hand show
that while there are almost no instances where people report their state as
completely unemotional, examples of full-blown emergent emotions are really
quite rare. As the ever present emotional life consists of moods, stances
towards objects and persons, and altered states of arousal, these indeed
should play a prominent role in emotion-related computational applications.
The envisaged scope of an emotion representation language clearly comprises
emotions in the broad sense, i.e.
should be able to deal with different emotion-related states.
There is an old Indian tale called "The blind men and the elephant" that enjoys some popularity in the psychological literature as an allegory for the conceptual difficulties to come up with unified and uncontroversial descriptions of complex phenomena. In this tale several blind men who never have encountered an elephant before, try to come up with an understanding of the nature of this unknown object. Depending on the body part each of them touches they provide strongly diverging descriptions. An elephant seems to be best described as a rope if you hang to its tail only, is a tree if you just touched its legs, appears as a spear if you encountered a tusk etc.
This metaphor fits nicely with the multitude of definitions and models
currently available in the scientific literature on emotions, which come
with a fair amount of terminological confusion added on top. There are no
commonly accepted answers to the questions on how to model the underlying
mechanism that are causing emotions, on how to classify them, on whether to
use categorial or dimensional descriptions etc. But leaving these questions
aside, there is a core set of components that are quite readily accepted
to be essentialcomponents of emergent
Subjective component: Feelings.
Feelings are probably what is most strongly associated with the term emotion in folk psychology and they have been claimed to make up an important part of the overall complex phenomenon of emotion.
Cognitive component: Appraisals
The most prominently investigated aspect of this component is the role of -- not necessarily conscious -- cognitive processes that are concerned with the evaluation of situations and events in the context of appraisal models (e.g. Arnold 1960, Lazarus 1966), i.e. the role and nature of cognitive processes in the genesis of emotions. Another aspect are modulating effects of emotions on cognitive processes, such as influences on memory and perception (e.g. narrowing of the visual field in fear).
Physiological changes both in the peripheral (e.g., heart-rate, skin-conductivity) and the central system (e.g. neural activity) are obviously one important component of emergent emotions. This component is also strongly interconnected with other components in this list: e.g. changes in the muscular tone, also account for the modulation of some expressive features in speech (prosody, articulatory precision) or in the appearance (posture, skin color).
Behavioral component: Action tendencies
Emotions have a strong influence on the motivational state of a subject. Frijda (1986) e.g. associated emotions to a small set of action tendencies, e.g. avoidance (relates to fear), rejecting (disgust) etc. Action tendencies can be viewed as a link between the outcome of an appraisal process and actual actions.
The expressive component comprises facial expressions but also body posture and gesture and vocal cues (prosody, voice quality, affective bursts).
Different theories may still strongly disagree on the relative importance of these components and on interactions and cause-and-effect relations between them. However, the fact that these components are relevant to some extent seems relatively uncontroversial.
Taking a software engineering approach to the question of how to represent
emotion in a markup language, the first necessary step for the Emotion
Incubator group was to gather a set of use cases for the language.
At this stage, we had two primary goals in mind: to gain an understanding of the many possible ways in which this language could be used, including the practical needs which have to be served; and to determine the scope of the language by defining which of the use cases would be suitable for such a language and which would not. The resulting set of final use cases would then be used as the basis for the next stage of the design process, the definition of the requirements of the language.
The Emotion Incubator group is comprised of people with wide ranging
interests and expertise in the application of emotion in technology and
research. Using this as a strength, we asked each member to propose one or
more use case scenarios that would represent the work they, themselves, were
doing. This allowed the group members to create very specific use cases based
on their own domain knowledge. Three broad categories were defined for these
Where possible we attempted to keep use cases within these categories, however, naturally, some crossed the boundaries between categories.
A wiki was created to facilitate easy collaboration and integration of
each member's use cases. In this document, subheadings of the three broad
categories were provided along with a sample initial use case that served as
a template from which the other members entered their own use cases and
followed in terms of content and layout. In total, 39 use cases were entered
by the various working group members: 13 for Data Annotation, 11 for Emotion
Recognition and 15 for Emotion Generation.
Possibly the key phase of gathering use cases was in the optimisation of the wiki document. Here, the members of the group worked collaboratively within the context of each broad category to find any redundancies (replicated or very similar content), to ensure that each use case followed the template and provided the necessary level of information, to disambiguate any ambiguous wording (including a glossary of terms for the project), to agree on a suitable category for use cases that might well fit into two or more and to order the use cases in the wiki so that they formed a coherent document.
In the following, we detail each broad use case category, outlining the range of use cases in each, and pointing out some of their particular intricacies. Detailed descriptions of all use cases can be found in Appendix 1.
The Data Annotation use case groups together a broad range of scenarios involving human annotation of the emotion contained in some material. These scenarios show a broad range with respect to the material being annotated, the way this material is collected, the way the emotion itself is represented, and, notably, which kinds of additional information about the emotion are being annotated.
One simple case is the annotation of plain text with emotion dimensions or categories and corresponding intensities. Similarly, simple emotional labels can be associated to nodes in an XML tree, representing e.g. dialogue acts, or to static pictures showing faces, or to speech recordings in their entirety. While the applications and their constraints are very different between these simple cases, the core task of emotion annotation is relatively straightforward: it consists of a way to define the scope of an emotion annotation and a description of the emotional state itself. Reasons for collecting data of this kind include the creation of training data for emotion recognition, as well as scientific research.
Recent work on naturalistic multimodal emotional recordings has compiled a much richer set of annotation elements (Douglas-Cowie et al., 2006), and has argued that a proper representation of these aspects is required for an adequate description of the inherent complexity in naturally occurring emotional behaviour. Examples of such additional annotations are multiple emotions that co-occur in various ways (e.g., as blended emotions, as a quick sequence, as one emotion masking another one), regulation effects such as simulation or attenuation, confidence of annotation accuracy, or the description of the annotation of one individual versus a collective annotation. In addition to annotations that represent fixed values for a certain time span, various aspects can also be represented as continuous "traces" -- curves representing the evolution of, e.g., emotional intensity over time.
Data is often recorded by actors rather then observed in naturalistic settings. Here, it may be desirable to represent the quality of the acting, in addition to the intended and possibly the perceived emotion.
With respect to requirements, it has become clear that Data Annotation
poses the most complex kinds of requirements with respect to an emotion
markup language, because many of the subtleties humans can perceive are far
beyond the capabilities of today's technology. We have nevertheless attempted
to encompass as many of the requirements arising from Data Annotation, not
least in order to support the awareness of the technological community
regarding the wealth of potentially relevant aspects in emotion
As a general rule, the context of the Emotion Recognition use case has to do with low- and mid-level features which can be automatically detected, either offline or online, from human-human and human-machine interaction. In the case of low-level features, these can be facial features, such as Action Units (AUs) (Ekman and Friesen 1978) or MPEG 4 facial action parameters (FAPs) (Tekalp and Ostermann, 2000), speech features related to prosody (Devillers, Vidrascu and Lamel 2005) or language, or other, less frequently investigated modalities, such as bio signals (e.g. heart rate or skin conductivity). All of the above can be used in the context of emotion recognition to provide emotion labels or extract emotion-related cues, such as smiling, shrugging or nodding, eye gaze and head pose, etc. These features can then be stored for further processing or reused to synthesise expressivity on an embodied conversational agent (ECA) (Bevacqua et al., 2006).
In the case of unimodal recognition, the most prominent examples are speech and facial expressivity analysis. Regarding speech prosody and language, the CEICES data collection and processing initiative (Batliner et al. 2006) as well as exploratory extensions to automated call centres (Burkhardt et al., 2005) are the main factors that defined the essential features and functionality of this use case. With respect to visual analysis, there are two cases: in the best case scenario, detailed facial features (eyes, eyebrows, mouth, etc.) information can be extracted and tracked in a video sequence, catering for high-level emotional assessment (e.g. emotion words). However, when analysing natural, unconstrained interaction, this is hardly ever the case since colour information may be hampered and head pose is usually not directed to the camera; in this framework, skin areas belonging to the head of the subject or the hands, if visible, are detected and tracked, providing general expressivity features, such as speed and power of movement (Bevacqua et al., 2006).
For physiological data, despite being researched for a long time
especially by psychologists, no systematic approach to store or annotate them
is in place. However, there are first attempts to include them in databases
(Blech et al., 2005), and suggestions on how they could be represented in
digital systems have been made (Peter and Herbon, 2006). A main difficulty
with physiological measurements is the variety of possibilities to obtain the
data and of the consequential data enhancement steps. Since these factors can
directly affect the result of the emotion interpretation, a generic emotion
markup language needs to be able to deal with such low-level issues. The same
applies to the technical parameters of other modalities, such as resolution
and frame rate of cameras, the dynamic range or the type of sound field of
the chosen microphone, and algorithms used to enhance the data.
Finally, individual modalities can be merged, either at feature- or
decision-level, to provide multimodal recognition. In this case, features and
timing information (duration, peak, slope, etc.) from individual modalities
are still present, but an integrated emotion label is also assigned to the
multimedia file or stream in question. In addition to this, a confidence
measure for each feature and decision assists in providing flexibility and
robustness in automatic or user-assisted methods.
We divided the 15 use cases in the generation category into a number of further sub categories, these dealt with essentially simulating modelled emotional processes, generating face and body gestures and generating emotional speech.
The use cases in this category had a number of common elements that represented triggering the generation of an emotional behaviour according to a specified model or mapping. In general, emotion eliciting events are passed to an emotion generation system that maps the event to an emotion state which could then be realised as a physical representation, e.g. as gestures, speech or behavioural actions.
The generation use cases presented a number of interesting issues that focused the team on the scope of the work being undertaken. In particular, they showed how varied the information being passed to and information being received from an emotion processing system can be. This would necessitate either a very flexible method of receiving and sending data or to restrict the scope of the work in respect to what types of information can be handled.
The first sub set of generation use cases were termed 'Affective Reasoner', to denote emotion modelling and simulation. Three quite different systems were outlined in this sub category, one modelling cognitive emotional processes, one modelling the emotional effects of real time events such as stock price movements on a system with a defined personality and a large ECA system that made heavy use of XML to pass data between its various processes.
The next sub set dealt with the generation of automatic facial and body gestures for characters. With these use cases, the issue of the range of possible outputs from emotion generation systems became apparent. While all focused on generating human facial and body gestures, the possible range of systems that they connect to was large, meaning the possible mappings or output schema would be large. Both software and robotic systems were represented and as such the generated gesture information could be sent to both software and hardware based systems on any number of platforms. While a number of standards are available for animation that are used extensively within academia (e.g., MPEG-4 (Tekalp and Ostermann, 2000), BML (Kopp et al., 2006)), they are by no means common in industry.
The final sub set was primarily focused on issues surrounding emotional speech synthesis, dialogue events and paralinguistic events. Similar to the issues above, the generation of speech synthesis, dialogue events, paralinguistic events etc. is complicated by the wide range of possible systems to which the generating system will pass its information. There does not seem to be a widely used common standard, even though the range is not quite as diverse as with facial and body gestures. Some of these systems made use of databases of emotional responses and as such might use an emotion language as a method of storing and retrieving this information.
The following represents a collection of requirements for an Emotion Markup Language ("EmotionML") as they arise from the use cases specified above. Each scenario described through the use cases has implicit requirements which need need to be made explicit to allow for their representation through a language. The challenge with the 39 use case scenarios collected in the Emotion Incubator group was to structure the extracted requirements in a way that reduces complexity, and to agree on what should be included in the language itself and what should be described through other, linked representations.
Work proceeded in a bottom-up, iterative way. From relatively unstructured lists of requirements for the individual use case scenarios, a requirements document was compiled within each of the three use case categories (Data Annotation, Emotion Recognition and Emotion Generation). These three documents differed in structure and in the vocabulary used, and emphasised different aspects. For example, while the Data Annotation use case emphasised the need for a rich set of metadata descriptors, the Emotion Recognition use case pointed out the need to refer to sensor data and environmental variables, and the use case on Emotion Generation requested a representation for the 'reward' vs. 'penalty' value of things. The situation was complicated further by the use of system-centric concepts such as 'input' and 'output', which for Emotion Recognition have fundamentally different meanings than for Emotion Generation. For consolidating the requirements documents, two basic principles were agreed on:
Based on these principles and a large number of smaller clarifications, the three use case specific requirements documents were merged into an integrated wiki document. After several iterations of restructuring and refinement, a consolidated structure has materialised for that document. The elements of that document are grouped into sections according to the type of information that they represent: (1) Information about the emotion properties, (2) Meta-information about the individual emotion annotations, (3) links to the rest of the world, (4) information about a number of global metadata, and (5) ontologies.
The language should not only annotate emergent emotions, i.e. emotions in
the strong sense (such as anger, joy, sadness, fear, etc.), but also
different types of emotion-related states.
The emotion markup should provide a way of indicating which of these (or
similar) types of emotion-related/affective phenomena is being annotated.
The following use cases require annotation of emotion categories and dimensions:
The emotion markup should provide a generic mechanism to represent broad
and small sets of possible emotion-related states. It should be possible to
choose a set of emotion categories (a label set), because different
applications need different sets of emotion labels. A flexible mechanism is
needed to link to such sets. A standard emotion markup language should
propose one or several "default" set(s) of emotion categories, but leave the
option to a user to specify an application-specific set instead.
Douglas-Cowie et al. (2006) propose a list of 48 emotion categories that
could be used as the "default" set.
The following use cases demonstrate the use of emotion categories:
The emotion markup should provide a generic format for describing emotions
in terms of emotion dimensions. As for emotion categories, it is not possible
to predefine a normative set of dimensions. Instead, the language should
provide a "default" set of dimensions, that can be used if there are no
specific application constraints, but allow the user to "plug in" a custom
set of dimensions if needed. Typical sets of emotion dimensions include
"arousal, valence and dominance" (known in the literature by
different names, including "evaluation, activation and power"; "pleasure,
arousal, dominance"; etc.). Recent evidence suggests there should be a fourth
dimension: Roesch et al. (2006) report consistent results from various
cultures where a set of four dimensions is found in user studies: "valence, potency, arousal, and unpredictability".
The following use cases demonstrate use of dimensions for representing emotional states:
Description of appraisal can be attached to the emotion itself or to an
event related to the emotion. Three groups of emotional events are defined in
the OCC model (Ortony, Clore, & Collins, 1988): the consequences of
events for oneself or for others, the actions of others and the perception of
The language will not cover other aspects of the description of events.
Instead, there will be a possibility to attach an external link to the
detailed description of this event according to an external representation
language. The emotion language could integrate description of events (OCC
events, verbal description) and time of event (past, present, future).
Appraisals can be described with a common set of intermediate terms between stimuli and response, between organism and environment. The appraisal variables are linked to different cognitive process levels in the model of Leventhal and Scherer (1987). The following set of labels (Scherer et al., 2004) can be used to describe the protagonist's appraisal of the event or events at the focus of his/her emotional state:relevance, implications Agency responsible, coping potential, compatibility of the situation with standards.
It should be possible to characterise emotions in terms of the action
tendencies linked to them (Frijda, 1986). For example, anger is linked to a
tendency to attack, fear is linked to a tendency to flee or freeze, etc. This
requirement is not linked to any of the currently envisaged use cases, but
has been added in order to cover the theoretically relevant components of
emotions better. Action tendencies are potentially very relevant for use
cases where emotions play a role in driving behaviour, e.g. in the behaviour
planning component of non-player characters in games.
The emotion markup should provide a mechanism to represent mixed
The following use cases demonstrate use of multiple and / or complex emotions:
The intensity is also a dimension. The emotion markup should provide an
emotion attribute to represent the intensity. The value of attribute
intensity is in [0;1].
The following use cases are examples for use of intensity information on emotions:
According to the process model of emotion regulation described by Gross
(2001), emotion may be regulated at five points in the emotion generation
process: selection of the situation, modification of the situation,
deployment of attention, change of cognition, and modulation of experiential,
behavioral or physiological responses. The most basic distinction underlying
the concept of regulation of emotion-related behaviour is the distinction of
internal vs. external state. The description of the external state is out of
scope of the language - it can be covered by referring to other languages
such as Facial Action Coding System (Ekman et al. 2002), Behavior Mark-up
Language (Vilhjalmsson et al. 2007).
Other types of regulation-related information can represent genuinely
expressed/felt (inferred)/masked(how well)/simulated, or inhibition/masking
of emotions or expression, or excitation/boosting of emotions or expression.
The emotion markup should provide emotion attributes to represent the
various kinds of regulation. The value of these attributes should be in
The following use cases are examples for regulation being of interest:
This section covers information regarding the timing of the emotion itself. The timing of any associated behaviour, triggers etc. is covered in section 4.3 "Links to the rest of the world".
The emotion markup should provide a generic and optional mechanism for
temporal scope. This mechanism allows different way to specify temporal
aspects such as i) start-time + end-time, ii) start-time+duration, iii) link
to another entity (start 2 seconds before utterance starts and ends with the
second noun-phrase...), iv) a sampling mechanism providing values for
variables at even spaced time intervals.
The following use cases require the annotation of temporal dynamics of emotion.:
The emotion markup should provide a mechanism to add special attributes
for acted emotions such as perceived naturalness, authenticity, quality, and
The emotion markup should provide a generic attribute enabling to
represent the confidence (or, inversely, uncertainty) of detection/annotation
or more generally speaking of probability to be assigned to one
representation of emotion to each level of representation (category,
dimensions, degree of acting, ...). This attribute may reflect the confidence
of the annotator that the particular value is as stated (e.g. that the user
in question is expressing happiness with confidence 0.8), which
is important especially in masked expressivity, or the confidence of an
automated recognition system with respect to the samples used for training.
If this attribute is supplied per modality it can be exploited in
recognition use cases to pinpoint the dominant or more robust of the existing
The following use cases require the annotation of confidence:
It represents the modalities in which the emotion is reflected, e.g. face, voice, body posture or hand gestures, but also lighting, font shape, etc.
The emotion markup should provide a mechanism to represent an open set of
The following use cases require the annotation of modality:
Most use cases rely on some media representation. This could be video files of users' faces whose emotions are assessed, screen captures of evaluated user interfaces, audio files of interviews, but also other media relevant in the respective context, like pictures or documents.
Linking to them could be
accomplished by e.g. an URL in an XML node.
The following use cases require links to the "rest of the world":
The emotion markup should provide a link to a time-line. Possible values of temporal linking are absolute (start- and end-times) and relative and refer to external sources (cf. 4.3.1) like snippets (as points in time) of media files causing the emotion.
Start- and end-times are important to mark onset and offset of an
The following use cases require annotation on specific positions on a time line:
The emotion markup should provide a mechanism for flexibly assigning meaning to those links.
The following initial types of meaning are envisaged:
We currently envisage that the links to media as defined in section 4.3.1 are relevant for all of the above. For some of them, timing information is also relevant:
The following use cases require annotation on semantics of the links to the "rest of the world":
Representing emotion, be it for annotation, detection or generation, requires the description of the context not directly related to the description of emotion per se (e.g. the emotion-eliciting event) but also the description of a more global context which is required for properly exploiting the representation of the emotion in a given application. Specifications of metadata for multimodal corpora have already been proposed in the ISLE Metadata Initiative [IMDI]; but they did not target emotional data and were focused on an annotation scenario.
The joint specification of our three use cases led to the identification of four groups of global metadata: information on persons involved, the purpose of classification i.e. the intended or used application, information on the technical environment, and on the social and communicative environment. Those are described in the following.
The following use cases require annotation of global metadata:
Information are needed on the humans involved. Depending on the use case, this would be the labeler(s) (Data Annotation), persons observed (Data Annotation, Emotion Recognition), persons interacted with, or even computer-driven agents such as ECAs (Emotion Generation). While it would be desirable to have common profile entries throughout all use cases, we found that information on persons involved are very use case specific. While all entries could be provided and possibly used in most use cases, they are of different importance to each.
The following use cases need information on the person(s) involved:
The result of emotion classification is influenced by its purpose. For
example, a corpus of speech data for training an ECA might be differently
labelled than the same data used for a corpus for training an automatic
dialogue system for phone banking applications; or the face data of a
computer user might be differently labeled for the purpose of usability
evaluation or guiding an user assistance program.These differences are
application or at least genre specific. They are also independent from the
underlying emotion model.
The following use cases need information on the purpose of the classification:
The quality of emotion classification and interpretation, by either humans or machines, depend on the quality and technical parameters of sensors and media used.
Also should the emotion markup be able to hold information on which way an
emotion classification has been obtained, e.g. by a human observer monitoring
a subject directly, or via a life stream from a camera, or a recording; or by
a machine, utilising which algorithms.
The following use cases need information on the technical environment:
The emotion markup should provide a global information to specify genre of the observed social and communicative environment and more generally of the situation in which an emotion is considered to happen (e.g. fiction (movies, theater), in-lab recording, induction, human-human, human-computer (real or simulated)), interactional situation (number of people, relations, link to participants).
The following use cases require annotation of the social and communicative environment:
Descriptions of emotions and of emotion-related states are heterogeneous,
and are likely to remain so for a long time. Therefore, complex systems such
as many foreseeable real-world applications will require some information
about (1) the relationships between the concepts used in one description and
about (2) the relationships between different descriptions.
The concepts in an emotion description are usually not independent, but are related to one another. For example, emotion words may form a hierarchy, as suggested e.g. by prototype theories of emotions. For example, Shaver et al. (1987) classified cheerfulness, zest, contentment, pride, optimism enthrallment and relief as different kinds of joy, irritation, exasperation, rage, disgust, envy and torment as different kinds of anger, etc.
Such structures, be they motivated by emotion theory or by
application-specific requirements, may be an important complement to the
representations in an Emotion Markup Language. In particular, they would
allow for a mapping from a larger set of categories to a smaller set of
The following use case demonstrates possible use of hierarchies of emotions:
Different emotion representations (e.g., categories, dimensions, and appraisals) are not independent; rather, they describe different parts of the "elephant", of the phenomenon emotion. Insofar, it is conceptually possible to map from one representation to another one in some cases; in other cases, mappings are not fully possible.
Some use cases require mapping between different emotion representations: e.g., from categories to dimensions, from dimensions to coarse categories (a lossy mapping), from appraisals onto dimensions, from categories to appraisals, etc.
Such mappings may either be based on findings from emotion theory or they
can be defined in an application-specific way.
The following use cases require mappings between different emotion representations:
The collection of use cases and subsequent definition of requirements presented so far was performed in a predominantly bottom-up fashion, and thus captures a strongly application centered, engineering driven view. The purpose of this section is to compare the result with a theory centered perspective. A representation language should be as theory independent as possible but by no means ignorant of psychological theories. Therefore a crosscheck to which extent components of existing psychological models of emotion are mirrored in the currently collected requirements is performed.
In Section 2, a
list of prominent concepts that have been used by psychologists in their
quest for describing emotions has been presented. In this section it is
briefly discussed whether and how these concepts are mirrored in the current
list of requirements.
Subjective component: Feelings.
Feelings have not been mentioned in the requirements at all.
They are not to be explicitly included in the representation for the moment being, as they are defined as internal states of the subject and are thus not accessible to observation. Applications can be envisaged where feelings might be of relevance in the future though, e.g. if self-reports are to be encoded. It should thus be kept as an open issue on whether to allow for an explicit representation of feelings as a separate component in the future.
Cognitive component: Appraisals
As a references to appraisal-related theories the OCC model (Ortony et al 1988), which is especially popular in the computational domain, has been brought up in the use cases, but no choice for the exact set of appraisal conditions is to be made here. An open issue is whether models that make explicit predictions on the temporal ordering of appraisal checks (Sander et al., 2005) should be encodable to that level of detail. In general, appraisals are to be be encoded in the representation language via attributing links to trigger objects.
The encoding of other cognitive aspects, i.e. effects of emotions on the cognitive system (memory, perception, etc.) is to be kept an open issue.
Physiological measures have been mentioned in the context of emotion recognition. They are to be integrated in the representation via links to externally encoded measures conceptualised as "observable behaviour".
Behavioral component: Action tendencies
It remains an issue of theoretical debate whether action tendencies, in contrast to actions, are among the set of actually observable concepts. Nevertheless these should be integrated in the representation language. This once again can be achieved via the link mechanism, this time an attributed link can specify an action tendency together with its object or target.
It was mentioned before that the representation language should definitively not be restricted to emergent emotions which have received most attention so far. Though emergent emotions make up only a very small part of the emotion-related states, they nevertheless are sort of archetypes. Representations developed for emergent emotions should thus be usable as basis for the encoding of other important emotion-related states such as moods and attitudes.
Scherer (2000) systematically defines the relationship between emergent emotions and other emotion-related states by proposing a small set of so-called design features. Emergent emotions are defined as having a strong direct impact on behaviour, high intensity, being rapidly changing and short, are focusing on a triggering event and involve strong appraisal elicitation. Moods e.g. are in contrast described using the same set of categories, and they are characterised as not having a direct impact on behaviour, being less intense, changing less quickly and lasting longer, and not being directly tied to a eliciting event. In this framework different types of emotion-related states thus just arise from differences in the design features.
It is an open issue whether to integrate means similar to Scherer's design features in the language. Because
probably not many applications will be able to make use of this level of
detail, simple means for explicitly defining the type of an emotion related
state should be made available in the representation language anyway.
Part of the activity of the group was dedicated to the assessment of
existing markup languages in order to investigate if some of their elements
or even concepts could fulfill the Emotion language requirements as described
in section 4. In the perspective of an effective Emotional Markup design it
will be in fact important to re-use concepts and elements that other
languages thoroughly define. Another interesting aspect of this activity has
been the possibility to hypothesize the interaction of the emotion markup
language with other existing languages and particularly with those concerning
Seven markup languages have been assessed, five of them are the result of W3C initiatives that led to recommendation or draft documents, while the remaining are the result of other initiatives, namely the projects HUMAINE and INTERFACE.
The assessments were undertaken when the requirements of the emotion
language were almost consolidated. To this end, the members of the group
responsible for this activity adopted the same methodology that basically
consisted in identifying among the markup specifications those elements that
could be consistent with the emotional language constraints. In some cases
links to the established Emotion Requirements were possible, being the
selected elements totally fulfilling their features, while in other cases
this was not possible even if the idea behind a particular tag could
nevertheless be considered useful. Sometimes, to clarify the concepts,
examples and citations from the original documents were included.
These analyses, reported in Appendix 2, were initially published on the Wiki page,
available for comments and editing to all the members of the incubator group.
The structure of these documents consists of an introduction containing
references to the analyzed language and a brief description of its uses. The
following part reports a description of the selected elements that were
judged as fulfilling the emotion language requirements.
The five W3C Markup languages considered in this analysis are mainly
designed for multimedia application. They deal with speech recognition and
synthesis, ink and gesture recognition, semantic interpretation and the
writing of interactive multimedia presentations. Among the two remaining
markup languages, EARL (Schröder et al., 2006), whose aim is the annotation
and representation of emotions, is an original proposal from the HUMAINE
consortium. The second one, VHML, is a language based on XML sub-languages
such as DMML (Dialogue Manager Markup Language), FAML (Facial Animation
Markup Language) and BAML (Body Animation Markup Language).
In detail, the existing markup languages that have been assessed are:
Many of the requirements of the emotion markup language cannot be found in
any of the considered W3C markups. This is particularly true for the emotion
specific elements, i.e. those features that can be considered the core part
of the emotional markup language. On the other hand, we could find
descriptions related to emotions in EARL and to some extent in VHML. The
first one in particular provides mechanisms to describe, through basic tags,
most of the required elements. It is in fact possible to specify the emotion
categories, the dimensions, the intensity and even appraisals selecting the
most appropriate case from a pre-defined list. Moreover, EARL includes
elements to describe mixed emotions as well as regulation mechanisms like for
example the degree of simulation or suppression. In comparison VHML, that is
actually oriented to the behavior generation use case, provides very few
emotion related features. It is only possible to use emotion categories (a
set of nine is defined) and indicate the intensity. Beyond these features
there is also the emphasis tag that is actually derived from the GML (Gesture
Markup Language) module.
Beyond the categorical and dimensional description of the emotion itself,
neither EARL nor VHML provide any way to deal with emotion-related phenomena
like for example attitudes, moods or affect dispositions.
The analyzed languages, W3C initiatives or not, offer nevertheless
interesting approaches for the definition of elements that are not strictly
related to the description of emotions, but are important structural elements
in any markup language. In this sense, interesting solutions to manage timing
issues, to annotate modality and to include metadata information were
Timing, as shown in the requirements section, is an important aspect in
the emotional language markup. Time references are necessary to get the
synchronization with external objects and when we have to represent the
temporal evolution of the emotional event (either recognized, generated or
annotated). W3C SMIL and EMMA both provide solutions to indicate absolute
timing as well as relative instants with respect to a reference point that
can be explicitly indicated as in EMMA or can also be an event like in SMIL
standard. SMIL has also interesting features to manage the synchronization of
Metadata is another important element included in the emotional markup.
The W3C languages provide very flexible mechanisms that could allow the
insertion of any kind of information, for example related to the subject of
the emotion, the trigger event, and finally the object, into this container.
Metadata annotation is available in SMIL, SSML, EMMA and VHML languages
through different strategies, from simple tags like the info element proposed
by EMMA (a list of unconstrained attribute-value couples) to more complex
solutions like in SMIL and SSML where RDF features are exploited.
Also referring to modality the considered languages provide different
solutions, from simple to articulated ones. Modality is present in SMIL,
EMMA, EARL and VHML (by means of other sub languages). They are generally
mechanisms that describe the mode in which emotion is expressed (face, body,
speech, etc.). Some languages get into deeper annotations by considering the
medium or channel and the function. To this end, EMMA is an example of an
exhaustive way of representing modalities in the recognition use case. These
features could be effectively extended to the other use cases, i.e.
annotation and generation.
Regarding interesting ideas, some languages provide mechanisms that are useful to manage dynamic lists of elements. An example of this can be found in the W3C PLS language, where name spaces are exploited to manage multiple sets of features.
This first year as a W3C Incubator group was a worthwhile endeavour. A
group of people with diverse backgrounds collaborated in a very constructive
way on a topic which for a considerable time appeared to be a fuzzy area.
During the year, however, the concepts became clearer; the group came to
an agreement regarding the delimitation of the emotion markup language to
related content (such as the representation of emotion-related expressive
behaviour). Initially, very diverse ideas and vocabulary arose in a bottom-up
fashion from use cases; the integration of requirements into a consistent
document consumed a major part of the time.
The conceptual challenges encountered during the creation of the
Requirements document were to be expected, given the interdisciplinary nature
of the topic area and the lack of consistent guidelines from emotion theory.
The group made important progress, and has produced a structured set of
requirements for an emotion markup language which, even though it was driven
by use cases, can be considered reasonable from a scientific point of view.
A first step has been carried out towards the specification of a markup language fulfilling the requirements: a broad range of existing markup languages from W3C and outside of W3C were investigated and discussed in view of their relevance to the EmotionML requirements. This survey provides a starting point for creating a well-informed specification draft in the future.
There is a strong consensus in the group that continuing the work is worthwhile. The unanimous preference is to run for a second year as an Incubator group, whose central aim is to convert the conceptual work done so far into concrete suggestions and requests for comments from existing W3C groups: the MMI and VoiceBrowser groups. The current plan is to provide three documents for discussion during the second year as Incubator:
If during this second year, enough interest from the W3C constituency is
raised, a continuation of the work in the Recommendation Track is
Arnold, M., (1960). Emotion and
Personality, Columbia University Press, New York.
Batliner, A., et al. (2006). Combining efforts for improving automatic
classification of emotional user states. In: Proceedings IS-LTC 2006.
Bevacqua, E., Raouzaiou, A., Peters, C., Caridakis, G., Karpouzis, K.,
Pelachaud, C., Mancini, M. (2006). Multimodal sensing, interpretation and
copying of movements by a virtual agent. In: Proceedings of Perception and Interactive
Blech, M., Peter, C., Stahl, R., Voskamp, J., Urban, B.(2005). Setting up
a multimodal database for multi-study emotion research in HCI. In: Proceedings of the 2005 HCI International
Conference, Las Vegas
Burkhardt, F., van Ballegooy, M., Englert, R., & Huber, R. (2005). An
emotion-aware voice portal. Proc. Electronic Speech Signal Processing
Devillers, L., Vidrascu, L., Lamel, L. (2005). Challenges in real-life
emotion annotation and machine learning based detection. Neural Networks 18,
Douglas-Cowie, E., et al. (2006). HUMAINE deliverable D5g: Mid Term Report
on Database Exemplar Progress. http://emotion-research.net/deliverables/D5g%20final.pdf
Ekman, P., Friesen, W. (1978). The Facial
Action Coding System. Consulting Psychologists Press, San Francisco
Ekman, P., Friesen, W. C. and Hager, J. C. (2002). Facial Action Coding System. The Manual on CD
ROM. Research Nexus division of Network Information Research
Frijda, N (1986). The Emotions. Cambridge: Cambridge University
Gross, J. J. (2001). "Emotion regulation in adulthood: timing is everything." Current Directions in Psychological Science 10(6). http://www-psych.stanford.edu/~psyphy/Pdfs/2001%20Current%20Directions%20in%20Psychological%20Science%20-%20Emo.%20Reg.%20in%20Adulthood%20Timing%20.pdf
Kopp, S., Krenn, B., Marsella, S., Marshall, A., Pelachaud, C., Pirker,
H., Thórisson, K., & Vilhjalmsson, H. (2006). Towards a common framework
for multimodal generation in ECAs: the Behavior Markup Language. In
Proceedings of the 6th International Conference on Intelligent Virtual Agents
Lazarus, R.S. (1966). Psychological stress and the coping process.
McGraw-Hill. New York.
Leventhal, H., and Scherer, K. (1987). The Relationship of Emotion to
Cognition: A Functional Approach to a Semantic Controversy. Cognition and
Ortony, A Clore, G.L. and Collins A (1988). The cognitive structure of emotions.
Cambridge University Press, New York.
Peter, C., Herbon, A. (2006). Emotion representation and physiology
assignments in digital systems. Interacting
With Computers 18, 139-170.
Roesch, E.B., Fontaine J.B. & Scherer, K.R. (2006). The world of
emotion is two-dimensional - or is it? Paper presented to the HUMAINE Summer
School 2006, Genoa.
Russell, J. A.. & Feldman Barrett L (1999). Core Affect, Prototypical
Emotional Episodes, and Other Things Called Emotion: Dissecting the
Elephant Journal of Personalityand Social Psychology,
Sander, D., Grandjean, D., & Scherer, K. (2005). A systems approach to
appraisal mechanisms in emotion. Neural Networks: 18, 317-352.
Scherer, K.R. (2000). Psychological models of emotion. In Joan C. Borod
(Ed.), The Neuropsychology of Emotion (pp. 137-162). New York: Oxford
Scherer, K. R. et al. (2004). Preliminary plans for exemplars: Theory.
HUMAINE deliverable D3c. http://emotion-research.net/deliverables/D3c.pdf
Schröder, M., Pirker, H., Lamolle, M. (2006). First suggestions for an
emotion annotation and representation language. In: Proceedings of LREC'06 Workshop on Corpora for
Research on Emotion and Affect, Genoa, Italy, pp. 88-92
Shaver, P., Schwartz, J., Kirson, D., and O'Connor, C. (1987). Emotion
knowledge: Further exploration of a prototype approach. Journal of Personality and Social
Tekalp, M., Ostermann, J. (2000): Face and 2-d mesh animation in MPEG-4.
Image Communication Journal 15,
Vilhjalmsson, H., Cantelmo, N., Cassell, J., Chafai, N. E., Kipp, M.,
Kopp, S., Mancini, M., Marsella, S., Marshall, A. N., Pelachaud, C., Ruttkay,
Z., Thórisson, K. R., van Welbergen, H. and van der Werf, R. J. (2007). The
Behavior Markup Language: Recent Developments and Challenges. 7th
International Conference on Intelligent Virtual Agents (IVA'07), Paris,
Wilhelm, P., Schoebi, D. & Perrez, M. (2004). Frequency estimates of emotions in everyday life from a diary method's perspective: a comment on Scherer et al.'s survey-study "Emotions in everyday life". Social Science Information, 43(4), 647-665.
The editors acknowledge significant contributions from the following persons (in alphabetical order):
Alexander is compiling a list of emotion words and wants to annotate, for each word or multi-word expression, the emotional connotation assigned to it. In view of automatic emotion classification of texts, he is primarily interested in annotating the valence of the emotion (positive vs. negative), but needs a 'degree' value associated with the valence. In the future, he is hoping to use a more sophisticated model, so already now in addition to valence, he wants to annotate emotion categories (joy, sadness, surprise, ...), along with their intensities. However, given the fact that he is not a trained psychologist, he is uncertain which set of emotion categories to use.
(i) Stephanie is using a multi-layer annotation scheme for corpora of
dialog speech, using a stand-off annotation format. One XML document
represents the chain of words as individual XML nodes; another groups them
into sentences; a third document describes the syntactic structure; a fourth
document groups sentences into dialog utterances; etc. Now she wants to add
descriptions of the 'emotions' that occur in the dialog utterances (although
she is not certain that 'emotion' is exactly the right word to describe what
she thinks is happening in the dialogs): agreement, joint laughter, surprise,
hesitations or the indications of social power. These are emotion-related
effects, but not emotions in the sense as found in the textbooks.
(ii) Paul has a collection of pictures showing faces with different
expressions. These pictures were created by asking people to contract
specific muscles. Now, rating tests are being carried out, in which subjects
should indicate the emotion expressed in each face. Subjects can choose from
a set of six emotion terms. For each subject, the emotion chosen for the
corresponding image file must be saved into an annotation file in view of
(iii) Felix has a set of Voice portal recordings and wants to use them to train a statistical classifier for vocal anger detection. They must be emotion-annotated by a group of human labelers. The classifier needs each recording labeled with the degree of anger-related states chosen from a bag of words.
Beneath this, some additional data must be annotated also:
(iv) Jianhua allows listeners to label the speech with multiple emotions to form the emotion vector.
(i) Jean-Claude and Laurence want to annotate audio-visual recordings of authentic emotional recordings. Looking at such data, they and their colleagues have come up with a proposal of what should be annotated in order to properly describe the complexity of emotionally expressive behaviour as observed in these clips. They are using a video annotation tool that allows them to annotate a clip using a 'chart', in which annotations can be made on a number of layers. Each annotation has a start and an end time.
The types of emotional properties that they want to annotate are many. They want to use emotion labels, but sometimes more than one emotion label seems appropriate -- for example, when a sad event comes and goes within a joyful episode, or when someone is talking about a memory which makes them at the same time angry and desperate. Depending on the emotions involved, this co-occurrence of emotions may be interpretable as a 'blend' of 'similar' emotions, or as a 'conflict' of 'contradictory' emotions. The two emotions that are present may have different intensities, so that one of them can be identified as the major emotion and the other one as the minor emotion. Emotions may be communicated differently through different modalities, e.g. speech or facial expression; it may be necessary to annotate these separately. Attempts to 'regulate' the emotion and/or the emotional expression can occur: holding back tears, hiding anger, simulating joy instead. The extent to which such regulation is present may vary. In all these annotations, a given annotator may be confident to various degrees.
In addition to the description of emotion itself, Jean-Claude and Laurence
need to annotate various other things: the object or cause of the emotion;
the expressive behaviour which accompanies the emotion, and which may be the
basis for the emotion annotation (smiling, high pitch, etc.); the social and
situational context in which the emotion occurs, including the overall
communicative goal of the person described; various properties of the person,
such as gender, age, or personality; various properties of the annotator,
such as name, gender, and level of expertise; and information about the
technical settings, such as recording conditions or video quality. Even if
most of these should probably not be part of an emotion annotation language,
it may be desirable to propose a principled method for linking to such
(ii) Stacy annotates videos of human behavior both in terms of observed behaviors and inferred emotions. This data collection effort informs and validates the design of our emotion model. In addition, the annotated video data contributes to the function and behavior mapping processes.
Cate wants to annotate the same clips as Jean-Claude (1c i), but using a different approach. Rather than building complex charts with start and end time, she is using a tool that traces some property scales continuously over time. Examples for such properties are: the emotion dimensions arousal, valence or power; the overall intensity of (any) emotion, i.e. the presence or absence of emotionality; the degree of presence of certain appraisals such as intrinsic pleasantness, goal conduciveness or sense of control over the situation; the degree to which an emotion episode seems to be acted or genuine. The time curve of such annotations should be preserved.
Dirk studies the ways in which persons in a multi-party discussion expresses their views, opinions and attitudes. We are particularly interested in how the conversational moves contribute to the discussion, the way an argument is settled, how a person is persuaded both with reason and rhetoric. He collects corpora of multi-party discussions and annotates them on all kinds of dimensions, one of them being a 'mental state' layer in which he tries to describe the attitudes that participants express with respect to what is being said and their emotional reactions to it. This layer includes elements such as: surprise, scepticism, anger, amusement, enthusiasm. He studies how these mental states are expressed and the functions of these expressions within the conversation.
Enrico wants to annotate a speech database containing emotional phrases. This material is used to extract prosodic models that will be used to appropriately select acoustic units in a corpus based speech synthesis system. The database consists of short sentences that are recorded from many speakers that read the scripts simulating certain emotional styles. Actually, each sentence is read in different emotional styles (e.g. sad, happy, angry, etc.) and a neutral style is also considered as the baseline. We want to study the acoustic correlations of the considered emotional styles in order to extract simple rules that account for the variation of some acoustic parameters. To achieve this, he needs to annotate the speech data, taking into account the intensity of the relative emotion and the level of valence.
In another case, Enrico wants to annotate pre-recorded illocutionary acts. Most of these prompts are frequently used expressions that have a pragmatic function such as greetings, thanks, regrets, disapprovals, apologies, compliments, etc. Given their intrinsic nature, these sentences are read in an expressive way. Enrico has to group these expressions into linguistic categories and describe them in terms of emotional intensity. For instance 'Good morning!' could be read in different ways: it could be happy, excited, or even sad. Moreover, given the emotional style, there could be different levels of intensity that could be described quantitatively using a range of values between 0 and 1.
Enrico wants to annotate para linguistics events such as laughs, sighs, pains or phenomena like these. These elements have to be described in terms of event category and of the emotion which they refer to. It could be useful also to describe quantitatively the effort of these events (for instance there could be 'weak' laughs or 'exaggerated' laughs).
Tanja recorded a video corpus where actors under the supervision of a
director were instructed to produce isolated sentences with 10 different
(categorically defined) emotions. In addition some of these emotions had to
be produced with i) increased intensity ii) decreased intensity and iii) in a
manner as the person would try to (unsuccessfully) hide/suppress her emotion.
This way for each sentence its intended emotion, intensity and
possible regulation attempts are already known and can be directly encoded.
In a next step ratings of human annotators are added, who are rating the
quality of the actors' performance: i) on how well the intended
emotional content can be actually perceived (i.e. this is some skewed variant
of 'annotator confidence') and ii) a rating of on how believability and
naturalness of the performance.
In the future extracts of the corpus should be used in classical rating experiments. These experiments may be performed on different combinations of modalities (i.e. full-body video, facial video, each with and without speech).
(i) (Speech emotion classifier): Anton has built an emotion classifier
from speech data which had been annotated in a way similar to use case 1b:
emotion labels were assigned on a per-word basis, and the classifier was
trained with the acoustical data corresponding to the respective word. Ten
labels had been used by the annotators, but some of them occurred only very
rarely. Based on a similarity metric, Anton merged his labels into a smaller
number of classes. In one version, the classifier distinguishes four classes;
in another version, only two classes are used. The classifier internally
associates various probabilities to class membership. The classifier can
either output only the one emotion that received the highest probability, or
all emotions with their respective probabilities. Classifier results apply in
the first step to a single word; in a second step, the results for a sentence
can be computed by averaging over the words in the sentence.
(ii) Felix has a set of Voice portal recordings, a statistical classifier,
a group of human labelers and a dialog designer. The aim is for the
classifier to give the dialog designer a detector of a negative user state
in several stages so that he/she can implement dialog strategies to deal with
the user's aggression. The training data should be annotated like in use case
1b (iii) and it should be possible to use it for for several dialog
applications (i.e. classifiers), so there must be mechanisms to map several
emotion categories and stages into each other.
(iii) Jianhua allows listeners to label the speech with multiple emotions
to form the emotion vector and then trains a classification tree model to
predict emotion vectors from acoustic features. The final emotion recognition
results are used in the dialogue system on line. The dialogue system uses the
results to determine the prior level the task from customers. Negative
emotions will result in quick service.
(iv) Juan is working on robots. The Automatic Speech Recognition module of his robot would be able to identify the emotional state of the speaker, not only to transcribe the uttered sentences. This emotional identification data could be used by the kernel to adapt the behavior of the robot to the new situation (for example, the identification of happiness traces in the voice of visitor could make the kernel change the dialogue in order to provide more information about the last items that could have been the cause of that happy state). The data to transfer should be the detected emotions (or ), the intensity levels and the confidence values associated to each detected emotion, and the time interval.
(i) (Multimodal emotion classifier): George has built a multimodal emotion
classifier that operates on facial, gesture and speech features. His main
issue is that facial features and gesture expressivity are usually annotated
on a frame level, gestures are described with timestamps in terms of phases,
and speech features may be annotated in terms of words, tunes or arbitrary
time windows. He would like to have an indication for each feature as to
whether it can be broken down in smaller chunks and still have the same value
or, inversely, be integrated across a wider window.
(ii) Christian is interested in software ergonomics and has built a system that tracks users' behaviour while operating software or using web pages. The system also collects emotion information on the user by use of several sensing technologies. The system is equipped with various sensors for both, behaviour tracking and emotion detection, like the following.
During a test, a user sits in the chair in front of the monitor and
performs a task. Unobtrusive sensors monitor her behaviour: the mouse
movements, mouse clicks, focal points, keyboard inputs; her posture and
movements in the chair, facial feature changes, and utterances; and ongoing
changes in her physiology. Robin also observes the user using the output of
the face camera, microphone, and a screen copy of the user's monitor. He
enters event markers into the system and adds comments on the user's
performance, environmental events like distracting sounds, spontaneous
assessments of the user's current emotions, or other observations he makes.
After the test, Robin also talks with her about her experiences, her likes
and dislikes on the software, and how she felt at particular situations using
the playback feature of his analysing tool. All the information collected are
of high value for Robin, who looks at the individual values of each modality
and input device, as well as the interrelations between them, their timely
order and changes over time. Robin also includes remarks on the user's
performance during the task and the results of the questionnaire, and puts
them in timely connection with the sensor data. Other information on the
setting, the software tested, environmental information like air temperature,
humidity, or air pressure, are available as meta data on the test as well.
Information on the subject, like gender, age, or computer experience, are
(iii) Jianhua builds a Audio-visual system. In traditional human computer
interaction, the lack of the coordination mechanism of parameters under
multi-model condition quite limits the emotion recognition. The fusing of
different channels is not just the combination of them, but to find the
mutual relations among them. Jianhua builds an emotion recognition system
which is based on audio-visual information. Both facial and audio data were
recorded, the detailed features, such as facial expression parameters, voice
quality parameters, prosody parameters, etc. were figured out. The mutual
relations between audio-visual information were also analyzed. With all above
work, the multimodal parameters were integrated into a recognition model.
(iv) Stacy works with ECAs. For the ECA's perception of other agents or
humans, there is a roughly inverse mapping process (inverse compared to
affective reasoning as in Use case 3a). That is, there are recognition
processes that map from the surface behavior of others to the behavioral
markup and then map the behavioral markup to a functional markup.
Robert envisages building a "Digital Radio Presenter application", using natural language and dialogue generation technology. The system would present radio shows which would include introducing music, interviewing guests and interacting with listeners calling in to the show.
Lori wants to train an audiovisual emotion classifier and needs to record data. She would like to associate user reactions with specific events happening to the user; so, she builds a simple computer game (e.g. a left-to-right space shooter) where the enemies can be controlled by the person responsible for the recordings. In this framework, sudden incidents occur (e.g. such as enemies appearing out of nowhere) inducing positive or negative reactions from the user.
Juan works an automatic person-machine interactive system (such as a robot) that could include a Natural Language module to identify the emotional state or attitude of the user by analyzing the sequence of words that have been recognized by the ASR (Automatic Speech Recognition) module or that have been written by the user in the computer interface.
As a result of this detection, if the automatic system has been insulted (one or more times) it should get progressively more and more angry; otherwise, when praised, the self esteem of the robot should go higher and higher. If the machine is really emotional, the interpretation of the emotional content can be influenced by the emotional state of the machine (when angry, it is more probable for the system to detect negative words in the text).
(i) Ruth is using an affective reasoning engine in an interactive virtual
simulation for children. Taking into account the current knowledge of the
virtual situation, the affective reasoner deduces the appropriate emotional
response. To do that, the situation is first analysed in terms of a set of
abstractions from the concrete situation, capturing the emotional
significance of the situation for the agent. These abstractions are called
'emotion-eliciting conditions' or 'appraisals' depending on the model used.
These 'appraisals' can then be interpreted in terms of emotions, e.g. emotion
(ii) Ian has developed an engine that uses a core functional property of
emotional behavior, to prioritize and pay attention to important real time
events within a stream of complex events, and wishes to apply this system to
the task of prioritizing real time stock quotes and alerting users to data
they, personally, would find important, surprising and interesting. A user
would personalize the system to match their own personality (or a different
one should they so wish) so the systems behavior would roughly match the
users own were they physically monitoring the real time stream of stock data.
The system would present the user with only that information it determined to
be interesting at any point in time. The presentation of data could be from a
simple text alert to a more complex visual representation. A central server
could receive the stream of real time events, assign values to each and then
send those packaged events to each user where their own, personally
configured, system would determine the importance of that particular event to
that particular user.
(iii) The cognitive-emotional state of ECAs (cf. UC 1c) inform their behavior in a multi step process. First the communicative intent and cognitive-emotional state of the agent is conveyed via an XML functional markup to a behavior generation process. That process in turn specifies a behavioral plan (surface text, gestures, etc) using a xml-based behavioral markup.
(i) Marc has written a speech synthesis system that takes a set of
coordinates on the emotion dimensions arousal, valence and power and converts
them into a set of acoustic changes in the synthesized speech, realized using
diphone synthesis. If the speech synthesizer is part of a complex generation
system where an emotion is created by an affective reasoner as in use case
3a, emotions must be mapped from a representation in terms of appraisals or
categories onto a dimensional representation before they are handed to the
(ii) Catherine has built an ECA system that can realize emotions in terms of facial expressions and gestural behavior. It is based on emotion categories, but the set of categories for which facial expression definitions exist is smaller than the list of categories that are generated in use case 3a. A mapping mechanism is needed to convert the larger category set to a smaller set of approximately adequate facial expressions. Catherine drives an ECA from XML tags that specifies the communicative functions attached to a given discourse of the agent. Her behavior engine instantiates the communicative functions into behaviors and computes the animation of the agent. The begin and end tags of each function mark the scope of the function. We synchronize communication function and speech in this way.
Given tags describing emotions, Catherine's difficulty is to translate
them into animation commands. She is looking for specification that would
help this process. For the moment we are using a categorical representation
(iii) Alejandra wants to build an ontology driven architecture that allows
animating virtual humans (VH) considering a previous definition of their
individuality. This individuality is composed of morphological descriptors,
personality and emotional state. She wants to have a module that
conceptualizes the emotion of a VH. This module will serve as input to
behavioral controllers that will produce animations and will update the
motioned emotion module. The main property that has to have this definition
of emotion is to allow plugging algorithms of behavior to allow the reuse of
animations and make comparisons with different models of behavior or
(iv) Ian has developed an engine that generates facial gestures, body
gestures and actions that are consistent with a given characters age, gender
and personality. In the application of a web based visual representation of a
real person Ian would like to allow users to add those visual representations
of their friends to their blog or web site for example. In order for each
character to represent its own user it needs to update the visual
representation, this can be achieved based on received 'event' data from the
user. Using this data a locally installed emotion engine can drive a 3D
character for example to represent the emotional state of a friend. Events
would be generated remotely, for example by actions taken by the friend being
represented, these events would be sent to the users local emotion engine
which would process the events, update the model of the friends emotional
state (emotion dimensions) and then map those dimensional values to facial
gesture, body gesture parameters and actions.
(v) Christine built a system that implements Scherer's theory to animate
an agent: going from a set of appraisal dimensions the system generates the
corresponding facial expressions in their specific time. Contrarily as when
using categorical representation the facial expression of the emotion does
not appear instantaneously on the face but facial regions by facial regions
depending on the appraisal dimensions that have been activated. She raised a
number of issues that are quite interesting and that are not specified in
Scherer's theory (for example how long does an expression of a given
appraisal dimension should last).
(vi) Jianhua generated an Emotional Speech System with both voice/prosody
conversion method (from neutral speech to emotional speech) and Emotion
Markup Languages (tags). The system is integrated into his TTS system and
used for dialogue speech generation in conversational system.
(vii) Jianhua also works on expressive facial animation. He is doing a new
coding method which can give more detailed control of facial animation with
synchronized voice. The coding system was finally transferred into FAPs which
is defined in MPEG-4. The coding method allows the user to configure and
build systems for many applications by allowing flexibility in the system
configurations, by providing various levels of interactivity with
(viii) The face, arms and general movement of Juan's robot could be
affected by the emotional state of the robot (it can go from one point to
another in a way that depends on the emotional state: faster, slower,
strength, etc.). The input would be the emotional state, the item (face,
arm...), the interval (it could be a time interval - to be happy from now to
then-, or a space interval -to be happy while moving from this point to that
point, or while moving this arm, etc.)
(ix) The Text To Speech module of Juan's robotic guide in a museum should accept input text with emotional marks (sent by the kernel or dialogue manager to the speech synthesiser): the intended emotions (or emotion representation values), the first and the last word for each emotion, the degree of intensity of the intended emotional expression. The TTS module could also communicate to the NL module to mark-up the text with emotional marks (if no emotional mark is present and the fully-automatic mode is active).
In this example, Enrico wants to insert pre-recorded illocutionary acts into a corpus based speech synthesis system. If appropriately used in the unit selection mechanism, these prompts could convey an emotional intention in the generated speech. The input text (or part of it) of the synthesis system should be annotated specifying the emotional style as well as the level of activation. The system will look for the pre-recorded expression in the speech database that best fits the annotated text.
Enrico wants to generate synthetic speech containing para linguistics events such as laughs, sighs, pains or phenomena like these, in order to strengthen the expressive effect of the generated speech. These events are pre-recorded and stored in the TTS speech database. The speech synthesis engine should appropriately select the best speech event from the database, given an effective annotation for it in the text that has to be synthesized. These events could be inserted at a particular point in the sentence or could be generated following certain criteria.
Robert envisages building a "Digital Radio Presenter application", using natural language and dialogue generation technology. The system would present radio shows which would include introducing music, interviewing guests and interacting with listeners calling in to the show.
According to http://www.w3.org/TR/SMIL/ SMIL has the following design goals.
Though SMIL is clearly designed for the purpose of encoding output-specifications, it nevertheless offers some interesting general purpose concepts.
In the overall design of SMIL much emphasis is put on defining it in terms of sub-modules that can be individually selected and combined for being directly used or embedded into other XML-languages.
This ability to be integrated in parts or as a whole into other XML-languages is a very desirable feature.
Though the degree of sophistication in SMIL probably is not necessary for our purpose (SMIL is split into more than 30 modules!), the design of SMIL should nevertheless be inspected in order to see how its modularity is achieved in technical terms (i.e. name spaces etc.)
Metadata in SMIL refers to properties of a document (e.g., author/creator, expiration date, a list of key words, etc.), i.e. it holds information related to the creation process of the document.
In the Emotional Language Requirements Meta data covers a more extended range of information types. Nevertheless, it is worthwhile to consider the SMIL Metadata as well, both in terms of XML syntax as well as in content.
SMIL provides two elements for specifying meta-data.
This is an empty element with the 2 attributes: name and content.
<smil:meta name="Title" content="The Wonderful EmoDataBase"/>
<smil:meta name="Rights" content="Copyright by Mr. Hide"/>
The choice for values of the attribute 'name' is unrestricted, i.e. any meta-data can be encoded BUT users are encouraged not to invent their own tags but to use the set of names from the "Dublin Core"-initiative.
"Dublin Core Metadata Initiative", a Simple Content Description Model for Electronic Resources, Available at http://dublincore.org/
Dublin Core Elements: http://dublincore.org/documents/usageguide/elements.shtml
This is new since SMIL 2.0 and now allows for the specification of metadata in RDF syntax. Its only sub-element is <rdf:RDF>, i.e. an element that holds RDF-specifications. It is claimed that (Quote) RDF is the appropriate language for metadata.RDF specifications can be freely chosen but again the usage of the (RDF-version of) Dublin Core metadata specification is encouraged.
This module deals with the specification of the synchronization of different media objects and thus provides one of the core-functionalities of SMIL. In SMIL the synchronization of objects is specified via (possibly) nested <seq> and <par> tags, enclosing media-objects that are to be presented in sequential and parallel order respectively. In addition to this sequential/parallel layout, for each media object start- and end-times can be specified either in terms of absolute values (e.g. start="2.5s") or in terms of events (start="movieXY.end+3.5s).
This mechanism for temporal layout is very attractive for all sorts of systems where multiple streams need to be synchronized. Most specifically it has inspired the implementation of timing modules in a number of representation languages for Embodied Conversational Agents (ECA).
This specification definitely is very handy for the purpose of the specification of timing in generation systems. It is very likely to be able to fulfill demands in the requirement regarding the Position on a time line in externally linked objects (section 4.3.2). Nevertheless it still needs to be evaluated whether this specification that is clearly biased towards generation should be part of the Emotion Markup Language.
A much more modest but still attractive candidate for re-using encodings from SMIL is the syntax for 'Clock Values', i.e. for time-values:
According to W3C SSML Recommendation 7 September 2004 (http://www.w3.org/TR/speech-synthesis ) the goal of this markup language is to provide a standard way to control different aspects in the generation of synthetic speech.
Current work on SSML is to define a version 1.1 which will better address internationalization issues. A SSML 1.1 first working draft was released on 10 January 2007 (http://www.w3.org/TR/speech-synthesis11 ). The publication of a second working draft is imminent.
SSML is oriented to a specific application that is speech synthesis, i.e. the conversion of any kind of text into speech. Consequently, the elements and attributes of this markup are specific to this particular domain. Only the meta, metadata and maybe desc elements could be considered as fulfilling the requirements of the Emotional Markup Language, while all the other elements refer to something that is outside of the emotion topic. On the other hand SSML should interact with "Emotion ML", speech being one of the available modalities in the generation of emotional behavior. By means of specific processing, the Emotional Markup annotation should be converted into an SSML document containing the constraints regarding, for example, the prosody of the speech that has to be synthesized.
The meta and metadata elements are used as containers for any information related to the document. The metadata tag allows the use of a metadata scheme and thus provides a more general and powerful mechanism to treat these typology of data. The meta element requires one of the two attributes "name" (to declare a meta property) or "http-equiv". A content attribute is always required. The only predefined property name is seeAlso and it can be used to specify a resource containing additional information about the content of the document. This property is modelled on the seeAlso property in Section 5.4.1 of Resource Description Framework (RDF) Schema Specification 1.0 RDF-SCHEMA .
<speak version="1.0" ...xml:lang="en-US"> <meta name="seeAlso" content="http://example.com/my-ssml-metadata.xml"/> <meta http-equiv="Cache-Control" content="no-cache"/> </speak>
The metadata element exploits a metadata schema to add information about the document. Any metadata schema is allowed but it is recommended to use the XML syntax of the Resource Description Framework (RDF) RDF-XMLSYNTAX in conjunction with the general metadata properties defined in the Dublin Core Metadata Initiative DC .
<speak version="1.0" ... xml:lang="en-US"> <metadata> <rdf:RDF xmlns:rdf = "http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:rdfs = "http://www.w3.org/2000/01/rdf-schema#" xmlns:dc = "http://purl.org/dc/elements/1.1/"> <!-- Metadata about the synthesis document --> <rdf:Description rdf:about="http://www.example.com/meta.ssml" dc:Title="Hamlet-like Soliloquy" dc:Description="Aldine's Soliloquy in the style of Hamlet" dc:Publisher="W3C" dc:Language="en-US" dc:Date="2002-11-29" dc:Rights="Copyright 2002 Aldine Turnbet" dc:Format="application/ssml+xml" > <dc:Creator> <rdf:Seq ID="CreatorsAlphabeticalBySurname"> <rdf:li>William Shakespeare</rdf:li> <rdf:li>Aldine Turnbet</rdf:li> </rdf:Seq> </dc:Creator> </rdf:Description> </rdf:RDF> </metadata> </speak>
Emotion ML might use a similar mechanism to address the metadata information related to the emotions.
The SSML desc element is used in conjunction with the audio element to add a description about the event itself. It is particularly useful when there is the need to textually explain paralinguistic information related to the audio. A mechanism like this could be generalized and used also in the emotion markup language to add descriptions to any generated event.
<speak version="1.0" ...xml:lang="en-US"> <voice xml:lang="de-DE"> <audio src="ichbineinberliner.wav">Ich bin ein Berliner. <desc xml:lang="en-US">Kennedy's famous German language gaffe</desc> </audio> </voice> </speak>
According to W3C EMMA working draft 9 April 2007 (http://www.w3.org/TR/emma/ ) this markup language is oriented to the interpretation of user input of a multimodal system.
As EMMA is an annotation scheme oriented to recognition applications, some of its elements and concepts could fulfill in particular the Use case 2 requirements of the emotional language markup. In the following paragraphs, only those EMMA specific elements that could be extended to the emotion markup are considered.
The main EMMA element is <emma:interpretation> . It comprises different attributes and values and holds a single interpretation represented in application specific markup. Each interpretation element is univocally identified by means of the "id" attribute (of type xsd:ID).
Cf. http://www.w3.org/TR/emma/#s3.3 These elements are used to manage the interpretations and to group them according to different criteria. EMMA considers three types of container elements:
The first one is used to indicate a set of mutually exclusive interpretations of the input and maybe it could be used in the emotion markup in Use case 2. The second container element is intended for multiple interpretations provided by distinct inputs (speech, gesture, etc.) but that are used for a common task. The last element is used for interpretations that are sequential in time. In the emotion markup these containers could be also used to manage interpretations. The one-of mechanism is useful when more results are available and a choice among them has to be carried out. The group concept could be generalized and used, for example, to treat multiple or complex emotions. The last container is also useful to describe the evolution of an emotional phenomenon.
Beyond these elements EMMA reports also the <emma:lattice> container, that is tightly linked to speech recognition applications. More interesting is the <emma:literal> element that is a child element of the interpretation and is used when the semantic results of the EMMA component are string literals without any surrounding application namespace markup. It could be useful also in the emotion markup to describe something not included in the application namespace.
<emma:interpretation> <emma:literal>boston</emma:literal> </emma:interpretation>
The <emma:model> is an annotation element used to express constraints on the structure and content of instance data and is specified as one of the annotations of the instance. It is identified by means of an "id" attribute, while a "ref" attribute is used to reference the data model. Within a single EMMA document, it is possible to refer to multiple data models. Since the emotion markup will consider different and also alternative representations to describe emotions, an element like the "model" could be used to manage different constraints to represent data. Models could also be used to manage domain specific sets of emotion categories or types.
<emma:model id="model1" ref="http://myserver/models/city.xml"/> <emma:interpretation id="int1" emma:model-ref="model1"> <city> London </city> <country> UK </country> </emma:interpretation> </emma:model>
The <emma:info> element acts as a container for vendor and/or application specific metadata regarding a user's input. In the emotion markup a tag like this could be a location for metadata. It could be used to add information about the subject and the object of the analyzed phenomenon/event. Moreover this tag can include markups that are not specific to EMMA, but something extensible and adaptable to specific requirements.
<emma:info> <caller_id> <phone_number>2121234567</phone_number> <state>NY</state> </caller_id> <customer_type>residential</customer_type> <service_name>acme_travel_service</service_name> </emma:info>
The <emma:process> attribute refers to the process that generates the interpretation. This annotation may include information on the process itself, such as grammar, type of parser, etc. There is no normative regarding the description of the process. This is something linked to the "rest of the world" in the emotion requirements and could be useful to indicate which process has produced the result that has to be interpreted, or also which process has to be used to generate the output, if we extend this concept to use case 3.
<emma:interpretation id="better" emma:process="http://example.com/mysemproc1.xml"> <origin>Boston</origin> <destination>Denver</destination> <date>tomorrow</date> </emma:interpretation>
The <emma:signal> attribute is a URI reference to the signal that originated the input recognition process while the <emma:media-type> attribute specifies the data format of the signal that originated the input. Also these attributes are links to the "rest of the world" and could be used to annotate, for example, audio and/or video sources.
<emma:interpretation id="intp1" emma:signal="http://example.com/signals/sg23.bin"> <origin>Boston</origin> <destination>Denver</destination> <date>03152003</date> </emma:interpretation> <emma:interpretation id="intp1" emma:media-type="audio/dsr-202212; rate:8000; maxptime:40"> <origin>Boston</origin> <destination>Denver</destination> <date>03152003</date> </emma:interpretation>
The emma:confidence attribute is a score in the range from 0.0 (minimum) to 1.0 (maximum) included, that indicates the quality of the input. It may state for the confidence of whatever processor was responsible for the creation of the EMMA result and it can also be used to assign confidences to elements in instance data in the application namespace. In the emotion language requirements this attribute is considered with the same meaning as in EMMA, and it could be used at different levels of representation and therefore could be applied to different elements.
<emma:interpretation id="meaning1" emma:confidence="0.6"> <destination emma:confidence="0.8"> Boston</destination> <origin emma:confidence="0.6"> Austin </origin> </emma:interpretation>
The emma:cost attribute is used to indicate the weight or cost associated with a user's input or part of it. It is conceptually related to the output of a recognition process when there are more interpretations. Values span from 0.0 to 10000000.
<emma:interpretation id="meaning1" emma:cost="1600"> <location>Boston</location> </emma:interpretation> <emma:interpretation id="meaning2" emma:cost="400"> <location> Austin </location> </emma:interpretation>
In Emma time references are indicated by using either relative or absolute timestamps. The time unit is the millisecond and absolute timestamps are the time in milliseconds since 1 January 1970 00:00:00 GMT. Absolute timestamps are indicated using the <emma:start> and <emma:end> tags. Regarding relative timestamps, EMMA defines the attribute <emma:time-ref-uri> that is a URI used to anchor the relative time and can be also an interval. The <emma:offset-to-start> attribute specifies the offset in milliseconds for the start of input from the anchor point. It is also possible to indicate timestamps relative to the end of the reference interval by setting the "end" value in the <emma:time-ref-anchor-point> attribute. Finally, the <emma:duration> attribute can be used to annotate the input duration and can be used independently of absolute or relative timestamps. In EMMA it is possible to have both absolute and relative timestamps in the same container.
<emma:interpretation id="int2" emma:time-ref-uri="#int1" emma:time-ref-anchor-point="start" emma:offset-to-start="5000"> <destination>Chicago</destination> </emma:interpretation>
Modality is a key concept in the emotion language. For annotating the input modality EMMA considers two attributes <emma:medium> and <emma:mode>. The first one is a sort of broad classification. Its values are acoustic, tactile, visual. The second attribute specifies the mode of communication through the channel (values: speech, dtmf_keypad, ink, video, photograph, ...). It is also possible to classify inputs with respect to their communicative function by using the <emma:function> attribute whose values are, for example : recording, transcription, dialog, verification, ...
<emma:one-of id="nbest1"> <emma:interpretation id="interp1" emma:confidence="0.6" emma:medium="tactile" emma:mode="ink" emma:function="dialog" emma:verbal="true"> <location>Boston</location> </emma:interpretation> <emma:interpretation id="interp2" emma:confidence="0.4" emma:medium="tactile" emma:mode="ink" emma:function="dialog" emma:verbal="false"> <direction>45</direction> </emma:interpretation> </emma:one-of>
According to W3C PLS (Pronunciation Lexicon Specification) second last call working draft 26 October 2006 (http://www.w3.org/TR/pronunciation-lexicon/ ), PLS is designed to enable interoperable specification of pronunciation information for both ASR and TTS engines within voice browsing applications.
The "role" attribute of the lexeme element (see Section 4.4) is the only reviewed aspect of the PLS language.
The values of the role attribute are based on QNAMES defined in Section 184.108.40.206 of XML Schema Part2: Datatypes Second Edition XML-SCHEMA . A QNAME or "qualified name" is composed of two parts separated by colon, where the first part is the qualification (a namespace prefix) and the second is a value defined in the namespace, e.g. "claws:VVI" for the value "VVI" in the namespace associated to the prefix "claws". The namespace guarantees that the values are unique and that they are extensible, if the namespace is changed, a different set of values is possible.
The QNAMES might be used to represent categorization that cannot be easily defined. In PLS the example were the Part-Of-Speech (POS), which are used in differnt ways in the NL and ASR communities.
This is an example of the use of the role attribute in PLS:
<?xml version="1.0" encoding="UTF-8"?> <lexicon version="1.0" xmlns="http://www.w3.org/2005/01/pronunciation-lexicon" xmlns:claws="http://www.example.com/claws7tags" alphabet="ipa" xml:lang="en"> <lexeme role="claws:VVI claws:VV0 claws:NN1"> <!-- verb infinitive, verb present tense, singular noun --> <grapheme>read</grapheme> <phoneme>ri:d</phoneme> <!-- IPA string is: "riːd" --> </lexeme> <lexeme role="claws:VVN claws:VVD"> <!-- verb past participle, verb past tense --> <grapheme>read</grapheme> <phoneme>red</phoneme> </lexeme> </lexicon>
This mark up language (http://www.w3.org/TR/2006/WD-InkML-20061023/ ) is quite far removed from the work of this group, being a specification for passing around information captured from pen like input devices.
It does share some of the high level concepts that we would like to have within the EmoXG group specification, namely:
It also has an emphasis on interoperability with other XML specifications, for example SMIL to allow for multi modal exchange of data.
The specifics of the markup language are bound to pen devices, which is not directly relevant for the Emotion markup language. Perhaps of interest is the way in which this is an example of a multi modal specification (http://www.w3.org/TR/mmi-reqs/ ).
Of further interest is in how their specification is put together, it seems similar in size and scope to what we would want to achieve and could be an interesting template. Their requirements document could also be a useful template (http://www.w3.org/TR/inkreqs/ ).
Of more interest is the Multi Modal Interaction guidelines (http://www.w3.org/TR/mmi-reqs/ ) which it seems we would be wise to follow if possible, an excerpt from the requirements document is relevant:
"We are interested in defining the requirements for the design of multi modal systems -- systems that support a user communicating with an application by using different modalities such as voice (in a human language), gesture, handwriting, typing, audio-visual speech, etc. The user may be considered to be operating in a delivery context: a term used to specify the set of attributes that characterizes the capabilities of the access mechanism in terms of device profile, user profile (e.g. identify, preferences and usage patterns) and situation. The user interacts with the application in the context of a session, using one or more modalities (which may be realized through one or more devices). Within a session, the user may suspend and resume interaction with the application within the same modality or switch modalities. A session is associated with a context, which records the interactions with the user."
Some of the key components of this specification are:
According to HUMAINE EARL language (Emotion Annotation and Representation Language) version 0.4.0, 30 June 2006 (http://emotion-research.net/earl ) this markup language is oriented to the representation and annotation of emotion in the use cases corpus annotation, recognition and generation of emotions in the first place.
This said, EARL is by definition highly related to the envisaged use cases and specification and provides many solutions to the named requirements. As general evaluation, EARL provides several highly valuable mechanisms and sets of items for the given requirements. The proposed ability of "plug-ins" seems a must, as well. The main drawback of EARL to be overcome is its lack of mechanisms for the description of Global Metadata and Classification Schemes for Emotions / Ontologies, as named in the EmoXG requirements. Some minor lacks are: no provision of emotion-related phenomenon, no real acting reference, sparse/no position on a time line and semantic links to the "rest of the world".
The next sections report a detailed evaluation by requirements with examples.
EARL does not allow for a specification of the emotion-related phenomenon as emotions, moods, interpersonal stances, etc.
EARL allows for "plug-ins" or dialects and provides presets for emotion categories that are valuable for re-consideration.
A set of 48 default categories is provided following Cowie et al.
These are provided within EARL. Suggested dimensions are arousal, power, valence.
<emotion xlink:href="face12.jpg" arousal="-0.2" valence="0.5" power="0.2"/>
These are also provided within EARL. 19 appraisals are suggested following Scherer's works.
<emotion xlink:href="face12.jpg" suddenness="-0.8" intrinsic_pleasantness="0.7" goal_conduciveness="0.3" relevance_self_concerns="0.7"/>
This is not covered by the EARL draft specification.
It is possible to attach several tags to one event.
<complex-emotion xlink:href="face12.jpg"> <emotion category="pleasure" probability="0.5"/> <emotion category="friendliness" probability="0.5"/> </complex-emotion>
It is possible to associate intensities for emotions.
<complex-emotion xlink:href="face12.jpg"> <emotion category="pleasure" intensity="0.7"/> <emotion category="worry" intensity="0.5"/> </complex-emotion>
Descriptors for regulation are also found in EARL.
<complex-emotion xlink:href="face12.jpg"> <emotion category="pleasure" simulate="0.8"/> <emotion category="annoyance" suppress="0.5"/> </complex-emotion>
Start/end time labels for emotions are as well included as a mechanism for continuous description of emotion changes in the FEELTRACE manner.
<emotion start="2" end="2.7"> <samples value="arousal" rate="10"> 0 .1 .25 .4 .55 .6 .65 .66 </samples> <samples value="valence" rate="10"> 0 -.1 -.2 -.25 -.3 -.4 -.4 -.45 </samples> </emotion>
No general mechanism exists with respect to acting apart from the regulation descriptors.
A probability tag is foreseen in EARL. In general, it is also possible to assign this probability to any level of representation.
<emotion xlink:href="face12.jpg" category="pleasure" modality="face" probability="0.5"/>
A modality tag exists in EARL and allows for assignment of emotion labels per modality.
<complex-emotion xlink:href="clip23.avi"> <emotion category="pleasure" modality="face"/> <emotion category="worry" modality="voice"/> </complex-emotion>
A general hyperref link mechanism allows for links to media. However, this is not intended to connect further media with objects, in the first place.
<complex-emotion xlink:href="face12.jpg"> ... </complex-emotion>
Apart from the possibility to assign emotion labels in start/end time and continuous manner, no links e.g. for recognition results in absolute and relative manner are provided.
Links to e.g. the experiencer, trigger of emotion, target of emotion, etc. are not included in EARL.
Mechanisms for the provision of none of the following is provided in EARL:
As for global meta-data
description, EARL is lacking the possibility to construct a hierarchy of
emotion words. Mapping mechanisms are also not provided.
The Virtual Human Markup Language (VHML) was created within the European Union 5th Framework Research and Technology Project InterFace . It is described in http://www.vhml.org/ . VHML is a markup language intended to be used for controlling VHs regarding speech, facial animation, facial gestures and body animation. It is important to notice that VHML has a simple representation of Emotion, however, it can be an example of the requirements formulated in Use case 3.
The next sections report a detailed evaluation by requirements with examples.
As VHML is for HCI using Virtual Humans, its representations can be considered as Affect dispositions.
Within EML used by VHML the categories of emotions used are: afraid, angry, confused, dazed, disgusted, happy, neutral, sad, surprised, default-emotion.
This aspect is not specified by VHML.
This aspect is not specified by VHML.
This aspect is not specified by VHML.
This aspect is not specified by VHML.
Intensity can be based on numeric values (0-100), or low-medium-high categories.
<afraid intensity="50"> Do I have to go to the dentist? </afraid>
Within the Gesture Markup Language (GML) of VHML, there is the definition of an emphasis element. Depending on the modality, speech or face, the element is synthesized.
<emphasis level="strong"> will not </emphasis> buy this record, it is scratched.
VHML specifies two temporal attributes for an emotion: 1. Duration, in seconds or milliseconds that the emotion will persist in the Virtual Human. 2. Wait, represents a pause in seconds or milliseconds before continuing with other elements or plain text in the rest of the document.
<happy duration="7s" wait="2000ms"/> It's my birthday today.
This aspect is not specified by VHML.
This aspect is not specified by VHML.
Modalities that can be established by referring to other ML: GML a gesture, FAML a facial animation, SML a spoken and BAML body animation.
<happy> I think that this is a great day. <smile duration="2s" wait="1s"/> <look-up> Look at the sky. There is <emphasislevel="strong">not a single </emphasis> cloud. </look-up> <agree duration="3500ms" repeat="4"/> The weather is perfect for a day at the beach. </happy>
EML allows having elements of the other markup languages to specify the modality.
This aspect is not specified by VHML.
This aspect is not specified by VHML.
VHML specifies the speaker of the text, regarding gender, age and category as well as with which emotion it is supposed to speak and act in general.
The person element contains the following attributes: age category (child, teenager, adult, elder), gender, name (specifies a platform specific voice name to speak the contained text), variant (specifies a preferred variant of another person to speak the contained text), disposition (specifies the emotion that should be used as default emotion for the contained text - the name of any of the EML elements).
<person age="12" gender="male" disposition="sad" variant="fred:1"> ... </person> <person variant="fred:2"> ... </person>
None of the following information can be explicitly indicated in VHML:
VHML is lacking the possibility to construct a hierarchy of emotion words and provide mapping mechanisms.