W3C Ubiquitous Web

W3C EmotionML Workshop — Minutes

5-6 October 2010

Hosted by Telecom ParisTech, Paris, France

EmotionML Workshop


  1. Gerard Chollet (Telecom ParisTech)
  2. Catherine Pelachaud (Telecom ParisTech)
  3. Kaz Ashimura (W3C/Keio)
  4. Marc Schröder (DFKI)
  5. Roddy Cowie (Queens University of Belfast)
  6. Isabella Poggi (Roma Tre University)
  7. Francesca D'Errico (Roma Tre University)
  8. Gill Windall (University of Greenwich)
  9. Sarah Jane Delany (Dublin Institute of Technology)
  10. Davide Bonardo (Loquendo)
  11. Felix Burkhardt (Deutsche Telekom)
  12. Laurent Ach (Cantoche)
  13. Takeshi Natsuno (Keio University)
  14. Masao Isshiki (W3C/Keio)
  15. Tim Llewellynnion (nViso)
  16. Yu Ding (Telecom ParisTech)
  17. Jing Huang (Telecom ParisTech)
  18. Quoc Anh Le (Telecom ParisTech)
Marc Schröder
Tim Llewellynn
Catherine Pelachaud
Marc Schröder
Laurent Ach
Sarah Jane Delany
Kaz Ashimura
Felix Burkhardt


Day1 - 5 October 2010

9:00-9:20 Session1: Welcome

Kaz Ashimura
Tim Llewellynn
1.What do we mean by emotion name?
2.What did we forget?
3.What is inappropriate/incomplete/redundant?
4.How can the current selection of “recommended” emotions vocabularies?
5.Should separate  element?
6.Is a single confidence for each emotion?
7.Currently modality lists only where the emotion was expressed : face, voice, body, text …
8.Currently all scale values are in the range (0,1) including both unipolar and bipolar scales.
9.How can EmotionML be used in HTML5?
EmotionML is a crossover to bring together two worlds. Psychologists /
researchers and at the other end businesses trying to make money with
money. EmotionML provides representation of emotions and related
states aimed to empower technology not to advance research on
emotion. Applications areas :

Opinion mining
Affective monitoring
Character design and control
Social robots
Expressive speech synthesis
Emotion recognition
Support for people with disabilities

9:20-10:55 Session2: Introduction

Kaz Ashimura
Tim Llewellynn

→ 20-min presentation

→ 40-min presentation

1.Is a single emotion categories good enough?

EmotionML does allow linkage and dynamic to be described.

2.How does the environment affect the emotion?

Emotion is a response to a mindscape not the landscape, so given the
same landscape different emotion can and do exist.

3.Can we develop lists that specifically address everyday emotions?

4.Can we expect emotion categories to solve all problems?

5.More generally, how do components relate to category descriptions?
Framing a satisfying description of emotional coloring is a huge

. Emotion split into “emergent” emotion and “pervasive”
  emotion. Technology has clear motives to engage with the emotional
  coloring that shapes people’s choices and values and how they feel
  about things. Mood, stance, and altered state of arousal makes >80%
  of users states. The things that are easy to describe are rare. The
  things that predominate are hard to describe. Emotional coloring is
  fundamental to human dialogue or oral interaction. Tools oriented to
  emergent emotion do not transfer simply to applications involving
  emotional coloring.

Major efforts have gone into lists of emergent emotions – often
hierarchies rather than lists and can be either theory, usage, or data
driven. Each approach arrives at different lists although names maybe
can’t describe all and every emotion. Large categories are not
practical to work with.

The temporal profile of an emotional state is important. Each emotion
has a temporal characteristic in addition to their instant once.

How robust are descriptors? 

Uncertainty is part of the picture because of mixed feelings,
unfamiliar feelings, concealment, and poor communication. This leads
to active perception not passive perception where linkage and dynamics
play are large role in understanding emotions.
Topics from Session2
Marc Schroeder
* Vocabularies
* Intensity
* Confidence
* Modality
* Neutral Point (scale)
* Relationship with HTML5

Roddy Cowie
* Distinguishing Problem
* Categories (based on data-driven approach)
* Intensity
* Caring
* Expression Tendency
* Components vs. Category Description
* Timing: Temporal Chracteristics
* Difiniteness
* Uncertainty: Active Perception
* Linkages: Network of feeling
* Dynamics of Feelings
* Landscape vs. Mindscape

(10:55-11:25 Morning Break)

11:25-13:00 Session3: Emotion Theories

Kaz Ashimura
Catherine Pelachaud

→ 15-min presentation x 2 (=30mins)

→ 60-min discussion including brief summarization of topics discussed during the session

Question: Mental Ingredients as a route to Emotion Markup Language
  — Isabella Poggi & Francesca D'Errico (Roma Tre University)

Marc: your talk shows that we talk a different language; challenge:
  how can we use EmotionML to represent some of the mental ingredients
  you described.

Marc: at the moment, EmotionML is not so ambitious. It uses a unified
  approach incorporating info from different theories.

  Some of the issues Isabella raises could be encompassed directly in
  the emotion category labels or other emotionML tags

  Where would the additional information Isabella has provided fit
  best? In the single emotion term?

  If this information is background information to understand an
  emotion, it does not need to be in EmotionML.

  Prediction information: part of EmotionML? Eg when you see somebody
  angry, you can predict he might bit you //à// how to encode this
  predicted information in EmotionML

Roddy mentions that Isabella: focus on goals and beliefs; so her
  primitives are different from those used in EmotionML. So what
  primitives do we need to do the type of studies Isabella mentions?

Marc: 2 suggestions: coding Isabella’s mental ingredients with
  EmotionML and apply Isabella’s analysis to the 17 everyday emotion

Question: The need to represent emotion-related states in EmotionML
  — Marc Schröder (DFKI)

Cf Scherer list of types of affect: emotions, moods, interpersonal
  stance, preferences/attitudes,

Roddy: emotion-related conditions; study of most-common types of
  emotion-related states

Marc: Does it exist dedicated vocabularies for these states? Eg for

(Roddy: they can be retrieved easily with some search in the

Mood: characterized by valence and time scale

Roddy: Moods tend to flicker between 2 emergent emotions; so the
  transition features of mood is very important.

Marc: do we have what we need to represent interpersonal stances?

Isabella: distinction between mood and interpersonal stance

Roddy: emotionML should stay away from interpersonal stances until we
  understand it better

Liz: However people want to use this term; if we want to have
  interoperability we need to establish a common list of terms. It
  will be more useful as people will use stances in their work

Roddy: stance is linked to attitude.

Marc: action point includes the word attitude in the glossary and
  states we do not use it as it is too complex and little is known
  about it

Gerard Chollet: why not using factor analysis to find out the
  dimensions of emotions?

Marc: Which types of emotion-related state to support in EmotionML?
  And which representations for these states?

Roddy: the notion of duration can be phrased with emotion inertia
  (inertia includes decay of an emotion-related states); consciously
Topics from Session3
* Mental Ingredients: Semantic Analysis vs. Conceptual Analysis
* Dimentions of emotions
* Common Ingredients:
 - Pride: dignity/superiority/arrogance
 - social relationships/purpose(function, goal)/admiration
* Definition of vocaburary vs. Markup Language itself
* What kind of primitives is needed?

* Emotion-related states
  - Types of affect
  - Emotion-related conditins
* Vocabularies for emotion-related states
  - need new definition?/reuse exixiting ones?
* Duration of state
* Transition potential, e.g., mood (anger, happiness)
* Interpersonal stance by category and target
* Which to support? (social emotion?)
* Dynamic emotion model: Inertia of emotion
* Social relationship
* Nesting of emotions vs. parallel emotions with time stamp

(13:00-14:00 Lunch)

14:00-15:30 Session4: Emotion Theories (Contd.)

Felix Burkhardt
Marc Schröder

→ 15-min presentaion x 2 (=30mins)

→ 60-min discussion including brief summarization of topics discussed during the session

Gill Windall: Tracking and Influencing Trainee Emotions in a
  Crisis-Planning Scenario

Pandora project: training people who manage crises on a strategic

Need to represent emotion in Pandora:
- trainees' emotional state (individual/group)
- trainees' initial state an demotional predisposition
- emotional change desired / target emotional state
- annotation of media with likely emotional impact
- emotion to be represented by Non-Player Characters

- may be useful to indicate sensor type or sensor id (issue:150), or
  use <reference> for that?

- represent relationships between  elements -- combined
  emotion is derived from the individual ones

- represnet timings with relation to both absolute and exercise
  timeline -- outside EmotionML?

- timing: "observed at" a given time, rather than start + end.

Emotional predisposition?

Desired emotional change (target state, or direction of change?)

Annotation of media with likely impact on perceiver; rules for
  combining effect of combined media?

Modality: modality elements may be easier to parse than a
  space-separated list of values in an attributes?

Scale: 0..1 or -1..1; continuous rather than discrete? linear rather
  than logarithmic? taking into account a "tipping point"?

Felix: very useful input. most important?

Gill: scales?

Tim: who gives meaning to the scale? self-report uses a five-point

Felix: a year ago we had a very complex scale model, now we wanted it
  as simple as possible

Gill: with real numbers, you get rounding errors

Felix: discrete scales have problem of agreeing on number and label of
  the discrete terms

Tim: maybe it would be important to know the origin of the number, a
  self-report or continuous source

Sarah Jane: danger of not agreeing on number of discrete values
  Issue of normalising the scale.

Marc: interpretation of the scale depends on the context.

Catherine: maybe the base level helps -- the "rest state" of the
  trainees in Pandora?

Catherine: in MPEG-4, they do not provide max values. only the minimum
  is specified.

Marc: if we adopted that, would it make interoperability easier or
  more difficult?

  About exaggeration, we had decided that it is the expression that is
  exaggerated for a cartoon character, not the emotion as such.

  Nobody is arguing in favour of exaggerated emotion values.

Tim: just it would be good to have the option of discrete values

Sarah Jane: it would make a difference in how to interpret the
  annotated data

Resulting requirement: Record labels and number of possible values for
  discrete scales.

Sarah Jane Delany: Use of Crowdsourceing for Labelling Emotional
  Speech Assets

Project: emotion detection in natural speech

High quality predictions require high quality recordings and high
quality annotations

Metadata annotation of recordings based on IMDI

Annotation: considering "crowdsourcing" (group performs task of
- Mechanical Turk; reCaptcha; "games with a purpose"; 
- new development in many different research areas is using
- "good" annotators are those that agree with the consensus rating
  (Brew et al 2010)

Training a system on annotations by "good" annotators converges much
more quickly on high quality predictions.

Use case: annotate speech assets using scales (incl. activation and
evaluation) using crowdsourcing.  Pilot test => need clear
instructions; asset selection strategy; level of payment.

Did use continuous scales but that made supervised learning more
difficult, so will move towards discrete scales (starting with simple
three-point scales).

Tim: we predict distributions rather than chosing a single best value

Requirement to represent multiple ratings of an asset (a speech clip).

Requirement to record the annotator.

=> Add an example into the EmotionML spec: individual ratings
   in <emma:group>, use emma:derived-from to state that the
   consolidated rating is based on the group of individual ratings

Topics from Session4
Univ. Greenwhich
* Relationship between trainer and trainee
* Wish list:
  - Sensor
  - Relationships between emotions
  - Single timepoint
  - base level
  - Combine media and markup
  - Scales: various types of scaples needed (discrete/non-linear/nominal etc.)

Dublin Institute of Tech.
* Prediction of emotion in natural language
  - acoustic
  - machine learning
* Mood inducted corpus of speech
* Croud-sourced annotations
* Needs:
  - Assets list
  - Reliability
  - Identify annotators
  - Individual vs. Consolidated

(15:30-16:00 Afternoon Break)

16:00-17:30 Session5: Use cases of Emotion Markup Language

Marc Schröder
Laurent Ach / Marc Schröder

→ 15-min presentaion x 3 (=45mins)

→ 45-min discussion including brief summarization of topics discussed during the session

Davide Bonardo (Loquendo)

Isabella Poggi: what about the performative of the Speech, in output
  specification? How do you simulate special voices for suggest, etc.

Davide Bonardo: performative can be used to control the expression of
  the avatar. TTS only use neutral, happy, sad

Isabella Poggi: but about suggest, etc. do you have specific voice
  parameters ? There is also something to do with stances. The way you
  pose yourself toward the other, you tell people to do something but
  you are uncertain. This is close to stance. Within performative,
  often some particular emotion is embedded. It means, if I order you,
  it means I am angry at you. In my View of ingredients, an emotion
  may be inside this order. I am anticipating something in my
  voice. How they can fit together ?

Davide Bonardo: Only affects can be described within emotion.

Catherine Pelachaud: in response to a question about the Dialog
  Manager : Project Partner for dialog management is As An Angel (they
  use APML)

Marc Schröder: For facial animation, the tradition is describing
  emotions with the big 6 emotions of Eckman. So the Computer Graphics
  community has used this in generation systems, driven from these 6
  basic emotions. It is considered an appropriate description. So a
  mapping is necessary. We have to distinguish mapping modalities from
  multimodal systems. Another point, in the voice, you have 6 levels
  of modalities : there is a conflict! Modalities don't match
  completely. Necessarily, a conversion must be done. There is an
  issue of mapping between different representations and it is not
  realistic to share the same representation any time soon.

Davide Bonardo: Actually there is a gap between the
  representations. There must be a link between them but this is not a
  problem. Each component uses the emotion it can understand.

Marc Schröder: eachTTS could say it accept some of the
  modalities. Different TTS will accept different inputs, it cannot be

Davide Bonardo: There must be a common language

Marc Schröder: in 3 dimensions, a prosody will be generated by a TTS
  and another TTS will generate another prosody. It is not yet the
  time to have a common format. The application chooses the
  format. EmotionML does not define a mapping between the different
  representations. OCC categories could be mapped with combination of
  appraisal dimensions. FSRE categories could be matched. It has not
  be done already. Some mapping are easy, others are impossible
  without losing information. EmotionML language could define a format
  to describe the mapping. It is not done yet.

Felix Burkhardt (Deutsche Telekom)

Isabella Poggi (about slides): what means "critical"?

Felix Burkhardt: it means substantial for the application


Sarah Jane Delany : Which labeled data do you keep?

Felix Burkhardt: I am not sure what to do: do some automatic
  classification and keep interesting ones, hope to find them. In my
  experience: 10% are really angry

Sarah Jane Delany: Active learning would be useful

Felix Burkhardt: Yes, good idea!


Gérard Chollet: there are cultural difference in speech

Felix Burkhardt: Yes


Tim Lieuvelynn: categories are written in specific languages?

Marc Shröder: they must be in English (their names come from the
  literature, which is in English). They can be translated for users
  in applications. Question about levels 1 to 6: no anger, not sure,
  and 3 levels of anger ... How do you represent that?

Felix Burkhardt: probably with 3 emotions. Anyway, level 2 is very

Marc Shröder: so 3 categories, one of them with intensities?

Felix Burkhardt: or linearly mapping ot ranges to 0 to 1

Roddy Cowie: people scale differently. It is very difficult to get
  uniform scales. It depends on the meanings you attach. This has
  implication on the meanings of scale in EmotionML.

Marc Shröder: you suggest to use discreet scales only?

Roddy Cowie: No. I mean that angry at level 4 is not necessarily twice
  as angry as level 2. Correlate people scores: you will see they use
  the intervals on the scale differently. People must understand this
  kind of limitations.

Laurent Ach (Cantoche)

- Living Actor: Virtual avatars on websites, serving as guides for

- Animation: direct edition of animation timeline; automatic animation
    from voice analysis; animation graph with emotion label;
    dimensions of emotion

- Affective avatars: Emotion recognition in user's voice, animate
    character in Skype plugin

Emotion first in research projects, then use the results in customer

Animate based on high-level annotations (semantics,
emotions). Mappings from emotion to animation is done by
artist. Dimensions of emotion indirectly related via selection of

Test at:

Roddy: How well does emotion recognition from text work?

Laurent: Company Lingware does it. In addition, we know a lot of
  things beforehand. In the second stage of the project, we will use
  different text, but not clear yet.

Tim: How do you choose your moods, where do they come from?

Laurent: Depends on the application; we do not distinguish precisely
  between emotions, moods, etc. They are high-level annotations. The
  artists realise these intuitively.

Marc: Dimensions -- do they trigger animations via thresholds?

Laurent: No, we have multiple continuous dimensions, and the
  animations have a number of different properties, so there is a
  selection algorithm.

Roddy: How is this related to persuasion?

Laurent: The goal is to lead the customer to different parts of the
  website. The approach is artistic and intuitive, there is no theory
  behind it.

Roddy: The theory exists -- would it be useful for you to have access
  to it?

Laurent: Yes. The artists are influenced by the theory.

Roddy: These things are around EmotionML, it is unclear if they should
  be integrated into EmotionML somehow.

Marc: Any specific requirements for EmotionML?

Laurent: No, what is already inside is fine with us. Actually:
  timestamps are currently absolute: it would be useful to have a
  point 0 (= when the web session is starting).

Tim: Same for us.
Topics from Session5
* Vocabulary for TTS
* Interoperability with SSML, etc.

Deutsche Telekom
* Technology push vs. Market pull
* VoiceXML should include EmotionML
* Global confidence is sufficient

* Emotion mapped to animation by artists

* Time code:
- currently absolute time
- would need a custom 0 point (at start of a Web session) 

(17:30 Day1 ends)

(19:00- Dinner)

Day2 - 6 October 2010

9:00-11:00 Session6: Use cases of Emotion Markup Language (Contd.)

Davide Bonardo
Catherine Pelachaud

→ 15-min presentaion x 3 (=45mins)

→ 45-min discussion including brief summarization of topics discussed during the session

Takeshi Natsuno (Dwango/Keio University)

Marc: What is the relation with EmotionML

Answer: 2 versions of the system: Html vs flash versions. Flash is
  customizable; in HTML we do not have all the tags as in flash

M: Is there a need for a specific interface: right now people can use
  quick text typing; may be an additional user interface is needed to
  add emotion tags

TN: the audience can feel the ‘air’ and can add more emotions by
  adding comments;

M: what’s about using emoticons

TN: people already use emoticon-like (LOL, applaud)

M: you do not hear your friend laughing; see your friend’s face

TN: so we could synchronize with the emotion of your friends; make
  more effects variety

M: real-time is a real pb in live applications; it needs to be fast,
  so users can not spend too much time to select display effects

Tim: When was the service started?

TN: 3 years ago; 7% of internet traffic is for nico nico

K: What is the link between emotion markup tags and arbitrary text
  written by people; emotion-related description/emoticon created by

  Need of video timestamps (SMIL/Media) and synchronization

  Relative position of information on video screen and EmotionML

Roddy: nico nico picks up on high level (speed, rhythm, number of
  comments at once)

  While emotionML acts on low level (smile)

  Emotion theorists may be able to help here.

Roddy: bond btn people is strong specially when their communication
  means is not understood by everyone.

Christian Peter (Fraunhofer IGD/Graz University)

Tim: where is the ground truth

Roddy: you can store the trace with emotionML; but you do not store
  the data related to material,

M: put such info in a global info tag

Tim Llewellynnion (nViso)

Masao: Can you detect when people are bored

Tim: we are using the big six

Roddy: there are existing models to detect other emotions (eg
  interest); you could add them in your software

Isabella: can you detect different type of emotion (eg diff type of
  laughs (happy smile, irony smile)

Tim: important for us is the pattern of the emotion profile

Isabella: so you trust the emotion that is annotated?

Marc: you predict from low-level feature, high level information? You
  could predict any other emotions rather than just the big 6?

Tim: some studies have correlated/validated the correspondence between
  low level features and emotions. We need other correspondences to
  larger variety of emotions.

Sarah-Jane: difficult to do classification for 7 classes in a go (6
  big six emotions + neutral)

Roddy: do you have analysis technique to find out different groups of
  people based on their reactions to adds.

Tim: We would like to do based on their emotional profile

Tim: We do not analyze the general set of Aus; we look for the
  features that are relevant in each of the big six emotions

Roddy: a lot of info is in the head and movement

Tim: we are starting looking into that

Roddy: there is an issue where features came in within EmotionML

Sarah: everybody will come with their own features as the most
  important ones

Roddy: there may be one level: the channel; feature point is one
  channel; head pose and posture is another channel; head dynamics

Felix: Can also be called modality (cf Christian Peter)

Roddy: needs of an agreed terminology to describe the channel/modality

Cath: cluster the channel/modality into group (visual/body,
  voice/acoustic, text, brain, physiological, etc)
Topics from Session6
* Relationship between arbitrary text and EmotionML

* Emoticons

* Stronger integration with SMIL and Media Fragment URI

* Relationship with position of emotion information on video screen
  and EmotionML

* Group of Emotion effects in NicoNicoDouga

* Comments are a databse which could be analyzed/reused

* trace of sensor output (sequencial data)

* grobal info tag/EMMA as container

* different reactions from different groups

* mapping between big six emtions and features

* face shape/action units

* visualization

* channels (modalities), e.g., facial points

* body parts grouping

(11:00-11:30 Morning Break)

11:30-13:00 Session7: Preliminary vote on topics & Scale discussion

Marc Schröder
Sarah Jane Delany

In this session we will first review topics discussed during the previous sessions, do a preliminary vote, and decide which topics to discuss in detail. And then we will have detailed discussion about those topics.

Tally of the preliminary vote
TopicFirst voteSecond vote
List of channels/modalities:8
Dynamics of feelings:7(defered to EmotionML 2.0)
Emotion-related states:20
Timing mechanism:23


Also we discussed "Scales" during session7.

Scale discussion
Discussion identified requirements for at least recording the number
of discrete categories in the scale and perhaps the labels used.

Roddy presented work on scales – Feeltrace scale

Asked people to identify appropriate labels that they found natural
and useful– didn’t use discrete categories but provided guidelines
to anchor the user.  Suggested use of an anchored continuous scale.
Divided into thirds with labels at each boundary.

None, mild social, strong, uncontrolled -­‐ are the markers/labels
that can be used for any emotion.

Bipolar scales – settled on two divisions with label of neutral in
middle.  Experience with more complex scales is that users don’t do
anything different.

Cross cultural differences in the rating of the intensity of the
emotion.  Is a health warning on cross cultural issues necessary?

Discussion on the anchored scaled raised 2 questions:
 - Do we want to use normalise the continuous scale using anchors?
 - Do we want to use a discrete scale?

The problem of accepting one group’s representation was raised as a

Discussion focussed on whether we set up reusable scales and how do we
recommend vocabularies.

Roddy advised that pyschologists have collections of standard scales
in databases and standard techniques for measurement.  Suggestion was
to find widely used scales and recommend use of them.

** Agreed to set up labels associated with a scale separately and use
   it in the vocabularies.  This can be done by pointing to the scale
   using a URI.

** Agreed to come up with best practice scales.

** Agreed to add to the dimensions, appraisals and action tendencies
   item, identification of the labels used for the scale.

** Agreed that a single value is given on the dimension which can
   either be discrete or continuous.

What to do with intensity was raised as an issue but there was no time
for discussion.
scale discussion

(13:00-14:00 Lunch)

14:00-16:00 Session8: Discussion on Vocabulary & Channels/Modalities

Marc Schröder
Kaz Ashimura (main group) / Catherine Pelachaud (smaller group)

We split into two subgroups. The main group discussed "Vocabulary" topic, and the smaller group consisting of Roddy and Catherine dicussed "Channels/Modalities" topic.

Vocabularies (main group)

davide: mentions PLS as example

sara-jane: example?

marc: let me try
... emotion is reaction to certain environment
... social regulation framework
... how to recognize emotion?
... keep tracing clean definitions
... depending on categories and context
... need custom vocabularies
... some vocabularies just make sense in some specific context
... can we agree to multiple vocabularies?

davide: vocabulary depends on services

marc: people look for orientations
... there is no default vocabularies
... FSRE vocabulary covers 4 features
... 24 categories
... we could consider language dependent mappings (for EmotionML 2.0?)

(some discussion on "default" vocabularies)

marc: the question is whether we want to make FSRE vocabulary a default or not

isabella: emotion in deep structure may be the only one that can allow reciprocal information with people
... because not only different language but also different theory
... besides the effort of mapping some terminology to others
... kind of like Don-Xhihote (spelling?)
... I think I can go on
... as for now, it could be a good candidate

kaz: one possible walk around is saying "an EmotionML processor SHOULD process FSRE"

marc: it would make sense to think about three major use cases again:
... manual annotation, recognition and generation

(Gill goes to the flipchart and write down the use cases)

marc: we can't ask all the users to revisit emotion theory and see the background of the vocabularies

sara-jane: is the purpose of EmotionML is interoperability?

marc: available category set depends on context of applications
... the syntax is quite different between read and write emotion information
... as soon as use actual application, our capability is limited
... so we should not require any specific set or subset of vocabulary

sara-jane: it should be best practice
... should not be mandated
... we should provide it just for people's easy use

[ kaz's note: wonders about sustainability of URIs for vocabulary set references ]

[ marc points out the next WD should discuss that point ]

felix: two questions?
... 1. do we need default?
... 2. rich set/minimum set?

gill: default minimum set?

felix: probably larger set would be safer

catherine: how about "big6"?

marc: there is something which can't be done by "big6" vocabulary
... we could say "this vocabulary set would fit several different purposes" without defining any "default" set

[ Catherine mentions the initial list of channels/modalities generated by Roddy and herself ]

CONSENSUS about default set: we could say "this vocabulary set would fit several different purposes" without defining any "default" set

won't make any default set mandatory

Channels/Modlities (Smaller group)
Effectors: static (pose) & dynamic (movement)
 - Gaze: looking at & mvt
 - Face
 - Head
 - Torso
 - Gesture
 - Leg
 - Locomotion/position of whole body
 - Speech: verbal, vocal & paraverbal

Visceral (controlled by autonomic nervous system): breathing, skin
colour, secretion (sweat, tear), pupil dilation

Central (central nervous system)
 - EEG
 - fMRIr
 - reflex (oculomotor reflex, startle reflex)
interaction? Turn-taking 

(16:00-16:20 Afternoon Break)

16:20-17:30 Session9: Discussion on Scale (revisit) & Intensity

Marc Schröder
Felix Burkhardt
Scale & label
Gill: scale labels, should they consist of pairs of labels and

- should there be one intensity for the emotion element?
- Could we treat categories just like dimension, having an attribute
  called "value" and allow for several in one emotion?
- should you be able to have "mixed emotions"?
- problem with mapping categoties to dimensional space.
- is emotion tag as being one single statement then still preserved? 
- discussion on correlation between intensity and confidence.
- suggestion: have value for categories as well as confidence.

(17:30 Workshop ends)

The Call for Participation, the Logistics Information, the Presentation Guideline and the Agenda are also available on the W3C Web server.

Marc Schröder, Catherine Pelachaud, Deborah Dahl and Kazuyuki Ashimura, Workshop Organizing Committee

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