Speech emotion recognition using Support Vector Machines

Thapanee Seehapoch · Sartra Wongthanavasu

124 citations0/0 files storedInternational Conference on Knowledge and Smart Technology

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

Abstract

Automatic recognition of emotional states from human speech is a current research topic with a wide range. In this paper an attempt has been made to recognize and classify the speech emotion from three language databases, namely, Berlin, Japan and Thai emotion databases. Speech features consisting of Fundamental Frequency (F0), Energy, Zero Crossing Rate (ZCR), Linear Predictive Coding (LPC) and Mel Frequency Cepstral Coefficient (MFCC) from short-time wavelet signals are comprehensively investigated. In this regard, Support Vector Machines (SVM) is utilized as the classification model. Empirical experimentation shows that the combined features of F0, Energy and MFCC provide the highest accuracy on all databases provided using the linear kernel. It gives 89.80%, 93.57% and 98.00% classification accuracy for Berlin, Japan and Thai emotions databases, respectively.

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References

2005A database of German emotional speech 2002Recognizing emotion in speech 2000Emotion Recognition in Speech Using Neural Networks 2004Automatic emotional speech classification 2011Automatic Speech Emotion Recognition using Support Vector Machine 2001Emotion Detection from Speech to Enrich Multimedia Content

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