Emotion in Speech

Research.Emotion History

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May 16, 2012 by -
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\title{Emotion in Speech}

\section{Speech Emotion Recognition}

Sponsor: IRCSET (Embark Initiative)

It is well known that much of the information conveyed in speech is non-verbal. In the past however, speech
recognition has focused almost exclusively on the words that were spoken, while disregarding the emotional
content. Automatic recognition of emotion from speech has many potential applications, from the design of more
user friendly human-machine interfaces to the improvement of speech recognition for natural speech. As this is a
relatively young field there remains uncertainty in the literature over the best classifier architectures and
feature sets for emotion classification, and even over how emotion should be represented within the clasifier

Early work in emotion recognition focused on the recognition of discrete emotional states (anger, joy, fear,
etc.). However, there is a growing belief that it may be more useful and powerful to classify speech along
affective dimensions (activation, valence, etc.). By associating discrete emotions with regions within this
activation-valence space we should be able to more effeciently represent a wider range of emotion.

To date, we have used Hidden Markov Models classifiers to explore the classification of speech along four
affective dimensions (activation, expectation, power and valence), and have compared classification performance
using different feature sets. Work is currently ongoing to extend this study in order to gain a more indepth
insight into how emotion is captured by different spectral, prosodic, and spectro-temporal features.
In the future we intend to explore alternative classifiers (for example Gaussian Mixture Models or Support Vector
Machines), and to eventually move toward continuous recognition of emotion.
Page last modified on May 16, 2012