Emotion recognition using multi-parameter speech feature classification

Speech emotion recognition is basically extraction and identification of emotion from a speech signal. Speech data, corresponding to various emotions as happiness, sadness and anger, was recorded from 30 subjects. A local database called Amritaemo was created with 300 samples of speech waveforms cor...

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Bibliographic Details
Published in2015 International Conference on Computers, Communications, and Systems (ICCCS) pp. 217 - 222
Main Authors Poorna, S. S., Jeevitha, C. Y., Nair, Shyama Jayan, Santhosh, Sini, Nair, G. J.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2015
Subjects
Cepstral analysis
cepstrum
Emotion
Emotion recognition
Feature extraction
hybrid rule based K-mean clustering
pitch
prosody
quefrency
Speech
Speech recognition
Support vector machines
SVM
Two dimensional displays
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Summary:Speech emotion recognition is basically extraction and identification of emotion from a speech signal. Speech data, corresponding to various emotions as happiness, sadness and anger, was recorded from 30 subjects. A local database called Amritaemo was created with 300 samples of speech waveforms corresponding to each emotion. Based on the prosodic features: energy contour and pitch contour, and spectral features: cepstral coefficients, quefrency coefficients and formant frequencies, the speech data was classified into respective emotions. The supervised learning method was used for training and testing, and the two algorithms used were Hybrid Rule based K-mean clustering and multiclass Support Vector Machine (SVM) algorithms. The results of the study showed that, for optimized set of features, Hybrid-rule based K mean clustering gave better performance compared to Multi class SVM.
DOI:10.1109/CCOMS.2015.7562904