A segment-based C-o adaptation scheme for PMC-based noisy Mandarin speech recognition

Abstract

A segment-based C-0 (the zero-th order of cepstral coefficient) adaptation scheme for PMC-based Mandarin speech recognition is proposed in this paper. It incorporates a new C-0 model of speech signal into the PMC method to improve the gain matching between the clean-speech HMM models and the current noise model. The C-0 model is constructed in the training phase by jointly modeling the normalized C-0 with other MFCC recognition features to form C-0-normalized HMM models. In the testing phase, it pre-segments the input utterance into syllable-like segments, performs C-0-denormaliztion operations to expand the C-0-normalized HMM models, and uses them in the PMC method. Compared with the conventional PMC method, the proposed method can achieve a much better noise compensation effect due to the use of more precise gain matching in the PMC model combination. Experimental results showed that the base-syllable accuracy rate was significantly upgraded for continuous noisy Mandarin speech recognition.