Speech emotion recognition: Features and classification models
Speech emotion recognition: Features and classification models
复制标题
语音情感识别：特征和分类模型
DOI：
10.1016/j.dsp.2012.05.007
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发表时间：
2012-12-01
影响因子：
2.9
通讯作者：
Cheng, Lee Lung
中科院分区：
文献类型：
--
作者：
Chen, Lijiang;Mao, Xia;Cheng, Lee Lung
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. (C) 2012 Elsevier Inc. All rights reserved.
