Comparison of Different Classifiers for Emotion Recognition

Theodoros Iliou · Christos‐Nikolaos Anagnostopoulos

58 citations0/0 files storedPanhellenic Conference on Informatics

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Emotion and Mood Recognition Speech Recognition and Synthesis Speech and Audio Processing

Abstract

In the present paper a comparison of two classifiers for speech signal emotion recognition is presented. Recognition was performed on emotional Berlin Database. Within this work we concentrate on the evaluation of a speaker-dependent and speaker independent emotion recognition classification. One hundred thirty three (133) speech features obtained from speech signal processing. A basic set of 35 features was selected by statistical method and artificial neural network and random forest classifiers were used. Seven classes were categorized, namely anger, happiness, anxiety/fear, sadness, boredom, disgust and neutral. In speaker dependent framework, artificial neural network classification reached an accuracy of 83,17%, and random forest 77,19%. In speaker independent framework, for artificial neural network classification a mean accuracy of 55% was reached, while random forest reached a mean accuracy of 48%.

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References

2011Praat: Doing Phonetics by Computer 2001Emotion recognition in human-computer interaction 2005A database of German emotional speech 2003Vocal communication of emotion: A review of research paradigms 2003Toward an affect-sensitive multimodal human-computer interaction 2005The role of intonation in emotional expressions 2002Prosody-based automatic detection of annoyance and frustration in human-computer dialog 2006Ensemble methods for spoken emotion recognition in call-centres

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2024In-depth investigation of speech emotion recognition studies from past to present –The importance of emotion recognition from speech signal for AI–2012Modeling phonetic pattern variability in favor of the creation of robust emotion classifiers for real-life applications

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