NDLI: Speech recognition in a reverberant environment using matched filter array (MFA) processing and linguistic-tree maximum likelihood linear regression (LT-MLLR) adaptation

Content Provider	IEEE Xplore Digital Library
Author	Raghavan, P. Renomeron, R.J. Che, C. Yuk, D.-S. Flanagan, J.L.
Copyright Year	1999
Description	Author affiliation: CAIP Center, Rutgers Univ., Piscataway, NJ, USA (Raghavan, P.)
Abstract	Performance of automatic speech recognition systems trained on close-talking data suffers when used in a distant-talking environment due to the mismatch in the training and testing conditions. Microphone array sound capture can reduce some mismatch by removing ambient noise and reverberation but offers insufficient improvement in performance. However, using array signal capture in conjunction with a hidden Markov model (HMM) adaptation on the clean-speech models can result in improved recognition accuracy. This paper describes an experiment in which the output of an 8-element microphone array system using MFA processing is used for speech recognition with LT-MLLR adaptation. The recognition is done in two passes. In the first pass, an HMM trained on clean data is used to recognize the speech. Using the results of this pass, the HMM model is adapted to the environment using the LT-MLLR algorithm. This adapted model, a product of MFA and LT-MLLR, results in improved recognition performance.
Starting Page	777
Ending Page	780
File Size	369980
Page Count	4
File Format	PDF
ISBN	0780350413
ISSN	15206149
DOI	10.1109/ICASSP.1999.759785
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1999-03-15
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Speech recognition Hidden Markov models Microphone arrays Adaptive arrays Automatic speech recognition Automatic testing System testing Acoustic noise Noise reduction Working environment noise
Content Type	Text
Resource Type	Article

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SPEECH RECOGNITION IN A REVERBERANT ENVIRONMENT USING MATCHED FILTER ARRAY (MFA) PROCESSING AND LINGUISTIC-TREE MAXIMUM LIKELIHOOD LINEAR REGRESSION (LT-MLLR) ADAPTATION

SPEECH RECOGNITION IN A REVERBERANT ENVIRONMENT USING MATCHED FILTER ARRAY (MFA) PROCESSING AND LINGUISTIC-TREE MAXIMUM LIKELIHOOD LINEAR REGRESSION (LT-MLLR) ADAPTATION P Raghavan (1999)

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