TY - GEN
T1 - Teager-Mel and PLP fusion feature based speech emotion recognition
AU - Chen, Xiao
AU - Li, Haifeng
AU - Ma, Lin
AU - Liu, Xinlei
AU - Chen, Jing
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/2/11
Y1 - 2016/2/11
N2 - Although a number of features derived from linear speech production theory have been investigated as speech emotion indicators, the recognition accuracy still stays unsatisfactory for realistic applications. In this paper, Teager-Mel, a novel speech emotion feature is proposed based on Teager Energy Operator (TEO) and the Mel perception characteristics. Due to such advantages as nonlinear and simple, TEO appears to be appropriate for speech emotion description. From the auditory psychophysical point of view, Perceptual Linear Predictive (PLP) features are also investigated as an extension to Teager-Mel. A Support Vector Machine (SVM) classifier is then adopted to the fusion of Teager-Mel and PLP features on a Chinese discrete emotional speech corpus (Dis-EC) that includes four emotions: happiness, anger, sorrow and surprise. Comparing with the previous studies based on prosodic features, the application of Teager-Mel features can achieve a recognition accuracy improvement of 10.4%, and similarly 8.2% for PLP features. The recognition accuracy reaches79.7% while using the fusion features, which appears to be the most attractive in relative researches.
AB - Although a number of features derived from linear speech production theory have been investigated as speech emotion indicators, the recognition accuracy still stays unsatisfactory for realistic applications. In this paper, Teager-Mel, a novel speech emotion feature is proposed based on Teager Energy Operator (TEO) and the Mel perception characteristics. Due to such advantages as nonlinear and simple, TEO appears to be appropriate for speech emotion description. From the auditory psychophysical point of view, Perceptual Linear Predictive (PLP) features are also investigated as an extension to Teager-Mel. A Support Vector Machine (SVM) classifier is then adopted to the fusion of Teager-Mel and PLP features on a Chinese discrete emotional speech corpus (Dis-EC) that includes four emotions: happiness, anger, sorrow and surprise. Comparing with the previous studies based on prosodic features, the application of Teager-Mel features can achieve a recognition accuracy improvement of 10.4%, and similarly 8.2% for PLP features. The recognition accuracy reaches79.7% while using the fusion features, which appears to be the most attractive in relative researches.
KW - Feature fusion
KW - Perceptual Linear Predictive (PLP)
KW - Speech Emotion recognition
KW - Teager Energy Operator (TEO)
UR - https://www.scopus.com/pages/publications/84963971810
U2 - 10.1109/IMCCC.2015.239
DO - 10.1109/IMCCC.2015.239
M3 - 会议稿件
AN - SCOPUS:84963971810
T3 - Proceedings - 5th International Conference on Instrumentation and Measurement, Computer, Communication, and Control, IMCCC 2015
SP - 1109
EP - 1114
BT - Proceedings - 5th International Conference on Instrumentation and Measurement, Computer, Communication, and Control, IMCCC 2015
A2 - Li, Jun-Bao
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Instrumentation and Measurement, Computer, Communication, and Control, IMCCC 2015
Y2 - 18 September 2015 through 20 September 2015
ER -