SVM-based separation of unvoiced-voiced speech in cochannel conditions

Unvoiced-voiced portions of cochannel speech contain considerable amounts of both voiced and unvoiced speech and play a significant role in separation. Motivated by recent developments in separation of speech from nonspeech noise, we propose a classification-based approach for unvoiced-voiced speech separation. A new feature set consisting of pitch-based features and gammatone frequency cepstral coefficients is proposed to represent the characteristics of a time-frequency unit. The cepstral features do not rely on pitch and are thus more robust than the pitch-based features to pitch estimation errors. Speaker-independent support vector machines are trained for classification. Results based on the TIMIT corpus show that the proposed algorithm significantly improves unvoiced speech segregation compared to a recent algorithm.