Comparison of Frequency-Warped Filter Banks in relation to Robust Features for Speaker Identification

摘要

Use of psycho-acoustically motivated warping such as mel-scale warping in common in speaker recognition task, which was first applied for speech recognition. The mel-warped cepstral coefficients (MFCCs) have been used in state-of-art speaker recognition system as a standard acoustic feature set. Alternate frequency warping techniques such as Bark and ERB rate scale can have comparable performance to mel-scale warping. In this paper the performance acoustic features generated using filter banks with Bark and ERB rate warping is investigated in relation to robust features for speaker identification. For this purpose, a sensor mismatched database is used for closed set text-dependent and text-independent cases. As MFCCs are much sensitive to mismatched conditions (any type of mismatch of data used for training evaluation purpose), in order to reduce the additive noise, spectral subtraction is performed on mismatched speech data. Also normalization of feature vectors is carried out over each frame, to compensate for channel mismatch. Experimental analysis shows that, percentage identification rate for text-dependent case using mel, bark and ERB warped filter banks is comparably same in mismatched conditions. However, in case of text-independent speaker identification, ERB rate warped filter bank features shows improved performance than mel and bark warped features for the same sensor mismatched condition. Also it is observed that, without any compensation (spectral subtraction or cepstral normalization) bark warped filter bank features, shows somewhat superior speaker identification results for both text-dependent and text-independent case.