Computer Science ›› 2015, Vol. 42 ›› Issue (9): 24-28.doi: 10.11896/j.issn.1002-137X.2015.09.005

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Speech Emotion Recognition Based on Acoustic Features

JIN Qin, CHEN Shi-zhe, LI Xi-rong, YANG Gang and XU Jie-ping   

  • Online:2018-11-14 Published:2018-11-14

Abstract: Emotion recognition from speech is a challenging research area with wide applications.This paper explored one of the key aspects of building an emotion recognition system:generating suitable feature representation.We extractedfeatures from four angles:(1)low-level acoustic features such as intensity,F0,jitter,shimmer,spectral contours etc.and statistical functions over these features,(2)a set of features derived from segmental cepstral-based features scored against emotion-dependent Gaussian mixture models,(3)a set of features derived from a set of low-level acoustic codewords,(4)GMM supervectors constructed by stacking the means or covariance or weights of the adapted mixture components on each utterance.We applied these features for emotion recognition independently and jointly and compared their performance within this task.We built a support vector machine(SVM) classifier based on these features.We testedthe performance of these different features on some public emotion recognition corpus(including IEMOCAP corpus in English,CASIA corpus in Mandarin,and BerlinEMO-DB in Germany).On the IEMOCAP database,the four-class emotion recognition accuracy of our system is 71.9%,which outperforms the previously reported best results on this dataset.

Key words: Speech emotion recognition,Acoustic features,Feature fusion

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