Filter bank based cepstral features for speaker recognition

Speaker recognition in mismatched condition is a vital issue in the recent years as training and testing speech data can be distorted due various practical conditions. Filter bank based cepstral features are used in many speech, speaker and language recognition tasks. These filter banks are mainly designed according to auditory based principles of speech processing, with variation in shape of filters and localization of their frequencies (center, left and right). The set of band pass filters can capture the information related to human vocal tract, which is one of the main distinguishing characteristics of individual. The non-uniform nature of (such as mel-scale warped) filter bank may cause loss of information in high frequency bands, which may carry some speaker specific information. Therefore uniformly (linearly) spaced filter bank cepstral coefficients can capture better speaker specific information, especially in mismatched conditions. In this paper, cepstral features derived from known psycho-acoustic filter banks (called MFCCs and BFCCs,) are compared with uniformly spaced filter bank cepstral coefficients (UFCCs) for text-dependent and text-independent cases. Experimental results shows that BFCCs are better in text-dependent case, whereas UFCC features give improved results than conventional MFCCs in case of text independent case, in mismatch condition. Results indicate that nature of filter bank plays an important role in extracting the speaker relevant features.