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Emotional Speech. Guest Lecturer: Jackson Liscombe CS 4706 Julia Hirschberg 4/20/05. Assumptions (1). Prosody is pitch ≈ fundamental frequency (f0) loudness ≈ energy (rms) duration ≈ speaking rate, hesitation Prosody carries meaning given/new focus discourse structure.

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emotional speech

Emotional Speech

Guest Lecturer: Jackson Liscombe

CS 4706

Julia Hirschberg


assumptions 1
Assumptions (1)
  • Prosody is
    • pitch ≈ fundamental frequency (f0)
    • loudness ≈ energy (rms)
    • duration ≈ speaking rate, hesitation
  • Prosody carries meaning
    • given/new
    • focus
    • discourse structure

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assumptions 2
Assumptions (2)
  • Text to Speech Synthesis (TTS)
    • formant-based
    • concatenative / unit selection
    • Articulatory
  • Machine learning techniques
    • predefined set of features
    • learn rules on a training corpus
    • apply rules to unseen data

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  • Why do we care about emotional speech?
  • Emotional Speech Defined
  • Perception Studies
  • Production Studies
  • Lauren Wilcox on voice quality

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emotion what is it good for
Emotion. What is it Good For?
  • Spoken Dialogue Systems
    • customer-care centers
    • task planning
    • tutorial systems
    • automated agents
  • Approaching Artificial Intelligence

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emotion why is it hard
Emotion. Why is it ‘hard’?
  • Colloquial def. ≠ Technical def.
  • Emotions are non-exclusive
  • Human consensus low

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study i consensus
Study I: Consensus
  • Liscombe et al. 2003
  • User study to classify emotional speech tokens
  • Semantically neutral (dates and numbers)
  • 10 emotions:
    • confident, encouraging, friendly, happy, interested
    • angry, anxious, bored, frustrated, sad
  • Example

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study i consensus8
Study I: Consensus

p < 0.001

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study i consensus9
Study I: Consensus
  • Emotions are heavily correlated
  • Emotions are non-exclusive
  • Are emotion labels appropriate?
    • activation
    • valency

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perception of emotional speech
Perception of Emotional Speech
  • Machine learning to predict emotional states in human speech
  • Common Features
    • prosody
    • lexical items
    • voice Quality

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acted speech
Acted Speech
  • 1990s - present
  • Aubergé, Campbell, Cowie, Douglas-Cowie, Hirscheberg, Liscombe, Mozziconacci, Oudeyer, Pereira, Roach, Scherer, Schröder, Tato, Yuan, Zetterholm, …

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study ii acted speech
Study II: Acted Speech
  • 4 actors
  • 10 emotions
  • Binary decision trees (RIPPER)
  • Accuracy ranged from 70% - 80%
  • Prosody indicative of anger, happy, sad
  • Voice quality indicative of anxious, bored

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emotional speech in spoken dialogue systems
Emotional Speech in Spoken Dialogue Systems
  • Batliner, Huber, Fischer, Spilker, Nöth (2003)
    • Verbmobil (Wizard of Oz scenarios)
  • Ang, Dhillon, Krupski, Shriberg, Stolcke (2002)
    • DARPA Communicator
  • Lee, Narayanan (2004)
    • Speechworks call-center
  • Prosodic, Lexical, and Discourse-level features

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study iii call center
Study III: Call-center
  • AT&T’s “How May I Help You” system
  • Predict anger and frustration

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study iii call center15
Study III: Call-center

“That amount is incorrect.”

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study iii call center18
Study III: Call-center
  • Feature sets
    • Prosodic (f0, rms, speaking rate)
    • Discourse (turn number, dialog act)
    • Lexical (words)
    • Contextual (dialogue history)

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study iv tutorial
Study IV: Tutorial
  • Physics tutorial system
  • Detect student uncertainty
  • Examples

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tts where are we now
TTS: Where are we now
  • Natural sounding speech for some utterances
    • Where good match between input and database
  • Still…hard to vary prosodic features and retain naturalness
    • Yes-no questions: Do you want to fly first class?
  • Context-dependent variation still hard to infer from text and hard to realize naturally:

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Appropriate contours from text
    • Emphasis, de-emphasis to convey focus, given/new distinction: I own a cat. Or, rather, my cat owns me.
    • Variation in pitch range, rate, pausal duration to convey topic structure
  • Characteristics of ‘emotional speech’ little understood, so hard to convey: …a voice that sounds friendly, sympathetic, authoritative….
  • How to mimic real voices?

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examples of emotional synthesis
Examples of Emotional Synthesis


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the role of voice quality in communicating emotion mood and attitude

L. Wilcox: Overview of Speech Communication paper for COMS4706

The Role of Voice Quality in Communicating Emotion, Mood, and Attitude

Christer Gobl, Ailbhe Ni Chasaide

Some slide content borrowed from

an online voice quality tutorialby K. Marasek

Experimental Phonetics Group

at the Institute of Natural Language Processing University of Stuttgart, Germany

voice quality
Voice Quality:
  • The characteristic auditory “coloring” of one’s voice
  • Derived from a variety of laryngeal and supralaryngeal features
  • Present throughout one’s speech.
  • The natural and distinctive tone of speech sounds produced by a particular person yields a particular voice (Trask 1996).
  • This paper focuses on harsh voice, tense voice, modal voice, breathy voice, whispery voice, creaky voice, and lax-creaky voice and the role of these voice qualities in affective expression.
  • The larynx is used to transform an airstream into audible sounds.

This process is central to perceived voice quality.

Most people in linguistics view voice qualities in terms of one quality in contrast with another.

Phonemic voice quality has a contrastive function in the phonological system of a language.

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-Subjects are asked to listen to synthesized utterances.

-Utterances were synthesized with seven different voice qualities.

-Subjects were asked to identify pairs of opposing affective attributes

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motivation for experiment
Motivation for experiment
  • Many vocal expressions signal affect: pitch variables, speech rate, pausing structure, duration of accented/unaccented syllables, these are easier to measure that voice quality
  • Voice quality is said to play a fundamental role in affective communication but few empirical studies seek to understand voice source correlates.
  • Some natural voice qualities said to map to affect and therefore assist in characterizing emotion in speech (based on phonetic observations)

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motivation for experiment29
-Different researchers have found varied mappings in their own empirical studies. Further study could confirm some previous findings:

Lavar ‘80, Scherer ‘86, Laukkanen ‘96

Breathy: intimacy

Whispery: confidentiality, secrecy

Harsh voice: anger

Tense voice: anger, joy, fear

Lax voice: sadness

But not all agree:

Murray, Arnott (’93)

Breathy: anger, happiness

Modal to tense: sadness

Motivation for Experiment

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motivation for experiment30
Motivation for Experiment

-Some findings conclude that glottal source contributes to the perception of valence as well as vocal effort (Laukkanen ‘97).

-Synthesis might be an ideal tool for examining how individual features of a signal contribute to the perception of affect.

-Previous work has generated emotive synthetic speech through manipulation of voice quality parameters (Cahn, ’90, Murray, Arnott ’95) but the synthesizers used didn’t offer full control of these parameters (DECtalk)

-Voice quality might signal strong as well as milder emotional states and speaker attitude

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Different speech source behaviors generate different voice qualities. Larynx adjusts in different ways to create different phonatory gestures, features

Laver (’80) defines three which are considered in this paper:

Adductive tension

(interarytenoid muscles adduct the arytenoid muscles)

Medial compression

(adductive force on vocal processes- adjustment of ligamental glottis)

Longitudinal pressure

(tension of vocal folds)

Recall scary glottis animation

 diagram online voice quality tutorialby

K. MarasekExperimental Phonetics Group at the

Institute of Natural Language Processing ,

University of Stuttgart, Germany

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modal voice
Modal voice

neutral mode

muscular adjustments are moderate

vibration of the vocal folds is periodic with full closing of glottis, so no audible friction noises are produced when air flows through the glottis.

frequency of vibration and loudness are in the lowto mid range for conversational speech

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tense voice voiced phonation
Tense voice – voiced phonation
  • Very strong tension of the vocal folds, very high tension in the vocal tract leads to harsh voice quality.

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whispery voice voiceless phonation
Whispery voice – voiceless phonation

Very low adductive tension

Medial compression moderately high

Longitudinal tension moderately high

Little or no vocal fold vibration

( produced through turbulences generated by the friction of the air in and above the larynx, which produces frication)

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creaky voice voiced phonation
Creaky voice – voiced phonation
  • vocal folds vibrate at a very low frequency – vibration is somewhat irregular, vibrating mass is “heavier” because of low tension (only the ligamental part of glottis vibrates)
  • The vocal folds are strongly adducted
  • longitudinal tension is weak
  • Moderately high medial compression
  • Vocal folds “thicken” and create an unusually thick and slack structure.

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lax creaky
Lax - creaky

Despite definition of creaky voice quality, creaky voice is found to have high glottal tension at times, and low tension at others

Different creaky quality, lax-creaky was created in experiment as separate from creaky.

Lax-creaky = breathy voice settings + reduced aspiration noise and added “creakiness” for experiment.

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breathy voice voiced phonation
Breathy voice – voiced phonation
  • Tension is low
  • minimal adductive tension,
  • weak medial compression
  • medium longitudinal tension of the vocal folds –folds do not come together completely leading to frication

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voice quality estimation is difficult
Voice quality estimation is difficult

If estimated with respect to a controlled neutral quality, how is that controlled quality known to be truly neutral? One must match the natural laryngeal behavior to the neutral model of behavior.

How adequate are the models of vocal fold movements for the description of real phonation?

The established relationships between a produced acoustical signal and the voice source are complex and since we are only able to observe the behavior of voicing indirectly, prone to error. Otherwise need direct source signal: obtained by invasive techniques (ouch) and invasion might interfere with signal.

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voice quality estimation
Voice quality estimation

Inverse filtering approach:

Speech production = source signal + vocal tract filter response

Inverse filtering cancels the effects of the vocal tracts, resulting signal is estimate of source – ill-posed problem

(popular approaches are automatic- based on linear predictive analysis – but do worse for non-modal (colorful) qualities

Still need to measure the inversely filtered signal

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-Subjects are asked to listen to synthesized utterances.

-Utterances were synthesized with seven different voice qualities.

-Subjects were asked to identify pairs of opposing affective attributes

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experiment details
Experiment - details

Natural utterances recorded in anehoic chamber ("anechoic" = "without echo”) high quality recording of the Swedish utterance “ja adjo” (semantically neutral) statement heard by non-swedish speaking native speakers of Irish English. The recording was digitized at high sampling frequency and high resolution (16bit) and prepared for analysis

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experiment details43
Experiment- details

Recorded utterance analyzed and parameterized. The popular LF (Liljencrants-Fant) model of differentiated glottal flow (Fant et al., 1995) was used to match the measured glottal waveform with a theoretical model of the voice source. Using LF: a waveform is described by a set of mathematical functions that model a given segment of the waveform. The following parameters were used in the experiment:

  • EE - excitation strength
  • RA – normalized value of TA - time constant of the exponential curve, describes the "rounding of the corner" of the waveform between t4 and t3 divided by t0 (amount of residual airflow after the main excitation prior to ax glottal closure.
  • RG – measure of glottal frequency as determined by the opening branch of the glottal pulse (normalized to fundamental frequency)
  • RK – measure of glottal pulse skew, defined by the relative durations of the opening and closing branches of the glottal pulse.

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experiment details44
Experiment - details

Utterance resynthesized with modal voice quality (moderate tension) formant synth (KLSYN88a synth Sensimetrics corp- Boston) allowing control of source and filter parameters and different variations of each

Once synthesized with modal voice, the modal stimuli is reproduced six times, each time with a different non-modal voice quality (tense, breathy, whispery, creaky, harsh, lax-creaky) . This is done by adjusting parameters such as

- fundamental frequency

  • Open Quotient (OQ) (ratio of the time in which the vocal folds are open and the whole pitch period duration)
  • Speed Quotient (also called skewness or rk)
  • (ratio of rise and fall time of the glottal flow

-more, differently to create different voice qualities

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experiment details45
Experiment - details
  • Perception tests constructed with each of the stimuli and given to subjects:

8 short subtests with 10 randomally chosen stimuli were given to subjects. Interval between sets: 7 secs

within each set of stimuli: 4 sec interval

  • Subjects respond to the affective content of the stimuli on a scale of 1 to 7 (opposite terms on either side): responses elicited for one particular pair of opposite affective attributes (bored vs. interested, friendly vs. hostile, sad vs. happy, intimate vs. formal, timid vs. confident afraid vs. unafraid)
  • 12 subjects partipicated: 6 male, 6 female

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Voice quality and subject variable were statistically highly significant

Differences between individual qualities were statistically significant

Most readily perceived:

Relaxation and stress

Highly perceived:

Anger, boredom, intimacy, content, formal

(aside from anger- these could be categorized as states, moods, attitudes, so consistent with experiment goal)

Least well perceived:

Unafraid, afraid, friendly, happy, sad

Milder states better signaled than strong emotion

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Notice modal stimuli is not perceived as totally neutral

Similar response patterns occurred with breathy/whispery and tense/harsh

Lax-creaky vs creaky does show significant differences

Results and their comparison to previous findings:

Lax-creaky: lower arousal, activation

Whispery: timid, afraid

Tense: high arousal/activation (confident, interested, happy, angry)

Breathy, whispery, creaky, and more so lax creaky: relaxed, content, intimate, friendly, sad, bored)

Lax-creaky, more so than whispery- effectively signaled intimacy

And lax-creaky, more so than breathy, signaled sadness Linking of breathy voice to anger and happiness were not supported

A shift from modal to tense elicited happy affect (rather than sad as proposed by Murray/Arnott ’99)

Anger is shown to link to tense voice and joy (Scherer ’86)

As one moves from high to low activation stimuli set, cross-subject variability increases

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some pros and cons of this study
Some pros and cons of this study

+ Showed that voice quality alone can evoke differences in speaker affect

  • But when comparing only synthesized voices, isn’t it a question of which is relatively more colorful?

+ voice qualities are multi-colored and each map to a variety of affective expression

(expressions are in some cases related, in others unrelated)

+ traditional view that voice quality conveys valence of emotion but not activation is challenged (for affective states with negative valence, activation still differentiates them and is detected with voice quality alone)

  • Hard to know to what degree naturally occurring phonomena matches model matches synthesis and which level to look at to improve or criticize when hearing final synthesis.
  • Aside from a phonetic system, subjects might associate voice qualities depending on personal situations, events, etc (could whispery sound sinister?)
  • When only deciding between 2 extremes, subjects might have difficulty trying “not” to listen for the purpose of choosing one or another (?)

- but same data reduction occurred, so beginning natural utterance not exact “copy”

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