Institute of Informatics of the Slovak Academy of Sciences. SPEECH IS MORE THAN ONLY ITS LINGVISTIC CONTENT. Rusko Milan. Institute of Informatics of the Slovak Academy of Sciences Dubravska cesta 9, 847 05 Bratislava, Slovakia Milan.R [email protected] E xpressive speech.
Institute of Informatics of the Slovak Academy of Sciences
Dubravska cesta 9, 847 05 Bratislava, Slovakia
“Expressive speech” designates the whole vocal display of a speaker.
Linguistic information part of information that can be encoded in general written text message
Various additional information on the speaker
– age, cultural background, education, sex, attempt, relation to the listener, individuality etc.
(The expression “individuality” is used here to denote personality, mood (attitude) and emotions of a speaker.)
- - > SPEECH - - >
Personality is considered to be a set of constant features of an individual.
Temperament is that aspect of personality that is genetically based, inborn.
personality p have n dimensions, and so it can be represented by a following vector (Egges, A., Kshirsagar, S., Magnenat-Thalmann, N.:
Five dimensions are enough to express the personality.
The Big Five model also known as OCEAN model takes into account the following five dimensions of personality:
(Digman, J. M, McRae, R.R.; John, O.P. )
Mood(attitude) can be defined as a rather static state of being, that is less static than personality and less fluent than emotions. Mood can be defined as one-dimensional (e.g. good or bad mood) or perhaps multi-dimensional (feeling in love, being paranoid etc.)
An emotional state has a similar structure as personality, but it changes over time.
Defined as an m-dimensional vector, where all m emotion intensities are represented by a value in the interval [0,1] .
The actual emotional state is dependent on the preliminary evolvement of emotins.
A need to model the emotins respecting their previous trends (history).
An emotional state history ωt is defined, that contains all emotional states until et, thus :
Egges continues with defining the individual ITas a triple (p, mt, et), where mt represents the mood of the individual at a time t.
Mood dimension is defined as a value in the interval [-1,1].
k mood dimensions=>the mood can be described as follows:
The mood and emotional values are changing in time
=>Both have to be updated regularly.
There are many theories of emotions and many different classifications exist.
This table, taken fromOrtony, A., Turner, T. J.  gives a short overview of basic emotion sets used by different authors.
Happy <======> Unhappy
Relaxed <======> Bored
Stimulated <======> Relaxed
Frenzied <======> Sluggish
Jittery <======> Dull
Controlling <======> Controlled
In control <======> Cared-for
Important <======> Awed
Autonomous <======> Guided
Semantic differential scales are often used for measuring emotion dimensions.
A Set of dimensions as proposed by Mehrabian & Russell (1974, Appendix B, p. 216).
It is evident that the authors have included moods and personality dimensions in this system too.
Problem: speech parameters involved in expression of personality, moods and emotions are shared for all the components of expressivity.
Decoding the expressive speech code is very subjective.
Nevertheless, a general set of the speech parameters responsible for the expression of emotion can be constructed. There are three main categories of speech correlates of emotion:
• Pitch contour
• Voice quality
It is believed that value combinations of these speech parameters are used to express vocal emotion.(Schröder M.)
Pitch contour is a representation of the intonation of an utterance, which describes the nature of accents and the overall pitch range of the utterance.
Pitch is expressed as fundamental frequency (F0).
One of the most frequently used methods for F0 measurement is the method using autocorrelation function of the LP residual.
Parameters include average pitch, pitch range, contour slope, and final lowering.
Models of intonation- two main categories:
The phonetic models (e.g. Fujisaki model, Tilt model, MOMEL and many others) model the intonation curve.
The phonological model (e.g. ToBI) is used to model the speaker's concept of distribution of accents in the intonational phrase.
The results of measurement of syllable andphoneme lengths are often given in a form of z-scores
(the instantaneous value is normalized be the mean value of the same elements in the whole database.
Parameters: speech rate, hesitation pauses, exaggeration...
Voice quality denotes the overall ‘character’ of the voice, which includes effects such as whispering, hoarseness, breathiness, and intensity.
The voice quality is influenced mainly by:
function of glottis
function of the vocal tract
A detailed classification scheme was published by Laver .
The analysis of the glottal function is generally done using source-filter model of speech production .
The glottal function is obtained from the speech signal by inverse filtering. One of the most efficient inverse filtering methods uses Discrete Linear Prediction – DLP (El-Jaroudi A., Makhoul J., )
to obtain the inverse filter coefficients and to filter the speech signal.
The resultant DLP residual function is considered as a representative of aderivative of glottal volume velocity function.
OQ, Open Quotient – ratio of the open phase of the glottal waveform to the period of the pulse.
OQ predicts the values for the amplitudes of the lower harmonics. (increased value of OQ is correlated with an increase in the amplitude of the lower harmonics in the voice spectrum.)
CQ, Closing Quotient – ratio of the closing phase of the glottal pulse to the period of the pulse.
These characteristics has been recently often replaced by AQ – Amplitude quotient and NAQ-Normalized amplitude quotient (Alku ).
EE, Excitation Strength – amplitude of the negative peak, calculated after the positive peak. EE is correlated with the overall intensity of the signal. A decrease in EE is correlated with a breathy voice.
RK, Glottal Symmetry/Skew – ratio of the closing phase to the opening phase of the differentiated glottal pulse. RK affects mainly the lower harmonics; the more symmetrical the pulse, the greater their amplitude.
H1-H2– the amplitude of the first harmonic (H1) compared to the amplitude of the second harmonic (H2). An indicator of the relative length of the opening phase of the glottal pulse (Hanson 1997).
H1-A1– the amplitude of the first harmonic (H1) compared to the strongest harmonic in the first formant (A1). Reflects the first formant bandwidth
spectral tilt - Expected to be large and positive for breathy voices and small and/or negative for creaky voices
H1-A2– the amplitude of the first harmonic (H1) compared to the amplitude of the strongest harmonic in the second formant (A2). An indicator of spectral tilt at the mid formant frequencies. Large and positive for breathy voices and small and/or negative for creaky voices.
H1-A3– the amplitude of the first harmonic (H1) compared to the amplitude of the strongest harmonic in the third formant (A3). An indicator of spectral tilt at the higher formant frequencies. Large and positive for breathy voices and small and/or negative for creaky voices.
Methods of vocal tract shape estimation include x-ray, computer tomography and magnetic resonance methods.
.Cheaper and quicker method – computing of the vocal tract shape from the speech signal
complementary to glottal pulse analysis from the speech signal. (e.g. vocal tract shape computation from LPC derived reflection coefficients).
- allows for analysis of the dynamic behavior of the articulators. Similar information can be obtained by formant analysis using homomorphic deconvolution (cepstrum) or LPC spectrum analysis.
Aim: to extract information from supra-segmental and extra-linguistic layers
Where to look for information:
a) long term characteristics
b) short term characteristics
a) glottal excitation function b) articulatory model
How to define a set of speech sound objects?
Speech sound event
Speech sound act
Speech sound gesture
Speech sound characteristic
Speech sound characteristic change
First steps to be accomplished:
data mining will be applicable:
Bag of words Bag of SSO
WordNet SSO semantic net