Speech emotion recognition: Features and classification models

Authors:
Lijiang Chen;Xia Mao;Yuli Xue;Lee Lung Cheng
Affiliations:
School of Electronic and Information Engineering, Beihang University, Beijing, China;School of Electronic and Information Engineering, Beihang University, Beijing, China;School of Electronic and Information Engineering, Beihang University, Beijing, China;Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China
Venue:
Digital Signal Processing
Year:
2012

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Abstract

To solve the speaker independent emotion recognition problem, a three-level speech emotion recognition model is proposed to classify six speech emotions, including sadness, anger, surprise, fear, happiness and disgust from coarse to fine. For each level, appropriate features are selected from 288 candidates by using Fisher rate which is also regarded as input parameter for Support Vector Machine (SVM). In order to evaluate the proposed system, principal component analysis (PCA) for dimension reduction and artificial neural network (ANN) for classification are adopted to design four comparative experiments, including Fisher+SVM, PCA+SVM, Fisher+ANN, PCA+ANN. The experimental results proved that Fisher is better than PCA for dimension reduction, and SVM is more expansible than ANN for speaker independent speech emotion recognition. The average recognition rates for each level are 86.5%, 68.5% and 50.2% respectively.