Wavelet packet analysis for speaker-independent emotion recognition

Abstract Extracting effective features from speech signals is essential to recognize different emotions. Recent studies have demonstrated that wavelet analysis is a useful technique in signal processing. In this study, we extract emotion features using wavelet packet analysis from speech signals for speaker-independent emotion recognition. We explore and evaluate these features from two databases, i.e., EMODB and EESDB. It is found that the extracted features are effective for recognizing various speech emotions. Furthermore, compared with common features such as Mel-Frequency Cepstral Coefficients (MFCC), these features can improve the recognition rates by 14.9 and 4.3 percentages on EMODB and EESDB, respectively.

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