emotional speech

Emotional Speech

Overview

Who cares? The Idea of Emotion Difficulties in approaching Describing Emotion Computational Models Modeling Emotion in Speech An example – Ang ’02

Who Cares?

Practical impact Detecting Frustration/Anger Stress/Distress Help call prioritizing Tutorials – Boredom/Confusion/Frustration

Pacing/Positive feedback User acceptance

Users preferred talking head using ES (Stallo, in Schröder)

Who Cares?

Esoteric Impact Is artificial intelligence possible w/o detection of

emotion? w/o display of “emotion”?

Do we experience someone/something as understanding us if it can’t understand our emotional state/experience?

Who Cares? – Izard ’77

Emotion & Perception E & Cognition E & Action E & Personality Development Understanding a speaker’s emotional state

gives us insight into his/her intention, desire, motivation (Zimring)

The Bad News (Picard ’97)

Maintaining realistic expectations User’s confidence in information Potential to forge affective channels Problem solving vs. empathic/observational Symmetry of communication Privacy issues

Idea of Emotion (Hergenhahn ’01)

Descartes “Passions”

Understood emotions as originating from bothphysiological and cognitive sources

Pineal gland

Late 1800’s – early 1900’s Psychology was study of consciousness

William James “The Science of Mental Life” Major method was introspection – mental

– Relies on a person reporting his/her experience

Idea of Emotion

1930’s – 1950’s Behaviorist tradition – study of behavior

“Objective” (at least measurable and observable) Emerged from academia – a lot of rats suffered Explains everything in terms of stimulus / response Fails to explain some crucial issues, e.g., language

Idea of emotion

1950’s – Cognitive “Revolution” Piaget, Miller, Chomsky, et al.

Miller The Science of Mental Life John Searle

Syntax vs. semantics Materialism vs. Dualism What are reasonable expectations?

No one expects to get wet in a pool filled with ping pong ball models

of water molecules.Searle ’90

Difficulties in approaching (Cowie)

E is resistant to capture in symbols Speech presents special problems Modeling of primary E’s not so useful Consensus Display Rules (Ekman) Mixes “Love/hate relationship” Negative response to simulated displays

Difficulties in approaching

Quality of reference data Rating believability (Schröder)

Forced choice tests often ignore issue of appropriateness/believability

“How appropriate was utterance to given E” (Rank 98)

(Iida, et al.) Rated using scales for preference and for subjective degree of expressed E.

Subject generosity Temporal and contextual relationships

“[Utterances were] said by two actors in the emotions of happiness, sadness, anger […]”

Pereira

Describing Emotion

Invariants

Everything it is possible to analyze depends on a clear method of distinguishing the

similar from the dissimilar.– Carl Linnaeus

= =

= ≠

≠

Describing Emotion (Cowie)

Primary emotions Acceptance, anger, anticipation, disgust, joy,

fear, sadness, surprise

Secondary Emotions Arousal Attitude An aside: Intention may generate all of these

activity decisiveness haughtiness restrained adoration delighted helplessness restraint alarm dependence hope righteousness alertness depression humiliation rigor anger desire indifference routine animosity despair inferiority sadness annoyance dimness initiative satisfaction anxiety disappointment intensity satisfied appetite disgust interest skepticism approval disqualification scorn artificiality disregard involvement serenity astonishment disrespect joy servility at ease distress leniency shame attraction droopy loneliness sharpness balanced embarrassment longing shyness belonging embitterment love simplicity bitterness enjoyment meditative sincerity bliss envy mirth sleepy restlessness blur exaggeration misery slumber boldness excitement sorrow boredom fatigue naturalness stability calmness fear nervousness stubbornness caution firmness pain suffering clearness frankness panic superiority compassion fondness passiveness surprise complexity friendly patience suspiciousness concern frustration pity sympathy conciliated gaiety pleasure tenderness confidence generosity posing tension constraint gloom pride tolerance hate confusion grateful quiescence tranquility contempt greediness regret uneasiness contentment grievance relaxed unstable courage guilt relief vigilance yearning craving happiness repulsion weakness criticism haste respect worry curiosity

Data of Emotion (Lang ’87)

Everyone generally agrees on existence Basic datum is a state of feeling

Completely private Include understanding of antecedents and

consequences Important to determine how E is represented

in memory Suggest a Turing test (but don’t describe…)

“…emotion is a fact upon which all introspection agrees. [Most emotional states] are states which

we have experienced personally.(Gellhorn & Loofbourrow ’63)

Describing Emotion

One approach: continuous dim. model (Cowie/Lang)

Activation – evaluation space Add control Curse of dimensionality Primary E’s differ on at least 2 dimensions of

this scale (Pereira)

Computational Models (Pfeifer ’87)

Emotion as process Emotion generation Influence of emotion Goal oriented nature Interaction between subsystems E. as heuristics Representation of emotion

Computational Models (Pfeifer ’87)

Examines models dimensionally A) Symbolic vs non-symbolic (cognitive vs AI) B) Augmented by emotion vs focused on emotion

All approaches deal with E as process Unclear whether system state = emotion Models must function in complex, uncontrollable,

unpredictable context No model for physiological aspect Emotions tightly coupled to commonsense reasoning

Modeling Emotion in Speech

Synthesis: basic issues (Schröder) How is a given emotion expressed? Which properties of the E state are to be

expressed? Relationship between this state and another

Approaches Formant synthesis (Burkhardt) Diphone concatenation Unit selection

Modeling Emotion in Speech

Formant synthesis (Burkhardt) High degree of control “emoSyn”

Mean pitch, pitch range, variation, phrase and word contour, flutter, intensity, rate, phonation type, vowel precision, lip spread

Two experiments Stimuli systematically varied, then classified Prototype generated and varied slightly

Modeling Emotion in Speech

Formant synthesis (Burkhardt) Fear

High pitch, broad range, falsetto voice, fast rate Joy

Broader pitch range, faster rate, modal or tense phonation, precise articulation

Lowest recognition rates (perhaps due to intonation patterns)

Boredom Lowered mean pitch, narrow range, slow rate, imprecise

articulation

Modeling Emotion in Speech

Formant synthesis (Burkhardt) Sadness

Narrow range, slow rate, breathy articulation Also raised pitch, falsetto Possible that sadness was imprecise term

Anger Faster rate, tense phonation

General results Recognition rates are comparable to natural speech,

especially when the categories from experiment 2 are recombined.

Modeling Emotion in Speech

Generally: tradeoff between flexibility of modeling and naturalness: Rule-based less natural Selection-based is less flexible

An Example – Ang ’02

Prosody-Based detection of annoyance/ frustration in human computer dialog

DARPA Communicator Project Travel Planning Data (a simulation) (NIST, UC Boulder, CMU)

Considers contributions of prosody, language model, and speaking style

Doesn’t begin with a strong hypothesis

An Example – Ang ’02

Uses recognizer output (sort of) Examines rel. of emotion and speaking style Uses hand coded style data

Hyperaticulation, pauses, raised voice

Repeated requests or corrections Hand labeled emotion relative to speaker

Original and consensus labels

An Example – Ang ’02

Emotion Class Instances Percent

NEUTRAL 41545 83.84%

ANNOYED 3777 7.62%

FRUSTRATED 358 0.72%

TIRED 328 0.66%

AMUSED 326 0.66%

OTHER 115 0.23%

NOT-APPLICABLE 3104 6.26%

Total 49553 100.0%

An Example – Ang ’02

Prosodic Features Duration and speaking rate Pause, pitch, energy, spectral tilt

Non-prosodic Features Repetitions & corrections Position in dialog

Language model features Discriminated using decision trees

“Brute force iterative algorithm” to determine useful features With and without LM features

An Example – Ang ’02

Ang ’02 – Decision Tree Usage

Temporal features 28% Longer duration, slow speaking rate corr.

w/ frustration

Pitch features 26% Generally, high F0 correlated w/ frustration

Repeats/corrections (= system error) 26% Correlated w/ frustration

Raised Voice

Ang ’02 – Results

Ang ’02 – Results

Performance better by 5-6% for utterances on which labelers originally agreed

Use of the repeat/correction feature improves success by 4%

Frustration vs Else – very little data Only slight difference between labeled and

recognized