A segment-based C0 (the zero-th order of cepstral coefficient) adaptation scheme for PMC-based Mandarin speech recognition is proposed in this paper. It incorporates a new C0 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 C0 model is constructed in the training phase by jointly modeling the normalized C0 with other MFCC recognition features to form C0-normalized HMM models. In the testing phase, it pre-segments the input utterance into syllable-like segments, performs C0-denormalization operations to expand the C0-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.
|Number of pages||4|
|Journal||ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings|
|State||Published - 1 Jan 1999|
|Event||Proceedings of the 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-99) - Phoenix, AZ, USA|
Duration: 15 Mar 1999 → 19 Mar 1999