Speech Emotion Recognition Based on Coiflet Wavelet Packet Cepstral Coefficients

A wavelet packet based adaptive filter-bank construction method is proposed for speech signal processing in this paper. On this basis, a set of acoustic features are proposed for speech emotion recognition, namely Coiflet Wavelet Packet Cepstral Coefficients (CWPCC). CWPCC extends the conventional Mel-Frequency Cepstral Coefficients (MFCC) by adapting the filter-bank structure according to the decision task; Speech emotion recognition system is constructed with the proposed feature set and Gaussian mixture model as classifier. Experimental results on Berlin emotional speech database show that the Coiflet Wavelet Packet is more suitable in speech emotion recognition than other Wavelet Packets and proposed features improve emotion recognition performance over the conventional features.