Increasing Speech Recognition Noise Robustness with HMM2

The purpose of this paper is to investigate the behavior of HMM2 models for the recognition of noisy speech. It has previously been shown that HMM2 is able to model dynamically important structural information inherent in the speech signal, often corresponding to formant positions/tracks. As formant regions are known to be robust in adverse conditions, HMM2 seems particularly promising for improving speech recognition robustness. Here, we review different variants of the HMM2 approach with respect to their application to noise-robust automatic speech recognition. It is shown that HMM2 has the potential to tackle the problem of mismatch between training and testing conditions, and that a multi-stream combination of (already noise-robust) cepstral features and formant-like features (extracted by HMM2) improves the noise robustness of a state-of-the-art automatic speech recognition system.


Published in:
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 02), I.929-932
Presented at:
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 02)
Year:
2002
Publisher:
Orlando, Florida, USA
Keywords:
Note:
IDIAP-rr 01-36
Laboratories:




 Record created 2006-03-10, last modified 2018-03-17

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