Audio Features Analysis for Speech Emotion Recognition

Saeeda Begum; Sana Ul Haq; Muhammad Saeed Shah; Muhammad Kamran; Imtiaz Rasool

Author(s)

Saeeda Begum Department of Electronics, University of Peshawar,Peshawar 25120,Pakistan

Sana Ul Haq Department of Electronics, University of Peshawar,Peshawar 25120,Pakistan

Muhammad Saeed Shah Department of Electronics, University of Peshawar,Peshawar 25120,Pakistan

Muhammad Kamran Department of Electronics, University of Peshawar,Peshawar 25120,Pakistan

Imtiaz Rasool Department of Electronics, University of Peshawar,Peshawar 25120,Pakistan

Abstract

In this paper audio features analysis is performed using two emotional speech databases: SAVEE in English language and EMO-DB in German language. A diverse set of more than 6000 acoustic features were extracted. The extracted features were normalized using z-score and min-max techniques, followed by feature selection using correlation, chi-square, gain ratio, and info gain ratio methods. Finally, classification was performed using various classifiers: support vector machine, Bayes net, meta and trees. The best classification result of 78.5% was achieved for seven emotion classes on Surrey audio-visual expressed emotion database using support vector machine classifier with 3500 features. The best result of 87.1% was obtained for the Berlin emotional speech database using support vector machine classifier with 4000 features. Classification performances comparable to human were obtained for both the databases. The Mel-spectrum, cepstral and spectral features were found most discriminative for audio emotion classification.

Publication Details

Page(s) 32-43

DOI DOI not available

Published Journal: The Sciencetech, Volume: 4, Issue: 4, Year: 2023

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Keywords

Chi square, Support Vector Machine, Info gain ratio, Feature selection, Speech emotion recognition

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