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 [BibTeX] [Marc21]
Increasing Speech Recognition Noise Robustness with HMM2
Type of publication: Idiap-RR
Citation: weber-rr-01-36
Number: Idiap-RR-36-2001
Year: 2001
Institution: IDIAP
Address: Martigny, Switzerland
Note: Published: ICASSP 2002,
Abstract: 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.
Userfields: ipdinar={2001}, ipdmembership={speech}, language={English},
Projects Idiap
Authors Weber, Katrin
Bengio, Samy
Bourlard, Hervé
Crossref by weber-ar-01-36
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Total mark: 0
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