Segment-based C0 adaptation scheme for PMC-based noisy Mandarin speech recognition

Wei Tyng Hong*, Sin-Horng Chen

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

5 Scopus citations


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.

Original languageEnglish
Pages (from-to)433-436
Number of pages4
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
StatePublished - 1 Jan 1999
EventProceedings of the 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-99) - Phoenix, AZ, USA
Duration: 15 Mar 199919 Mar 1999

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