Histogram Equalization to Model Adaptation for Robust Speech Recognition

We propose a new model adaptation method based on the histogram equalization technique for providing robustness in noisy environments. The trained acoustic mean models of a speech recognizer are adapted into environmentally matched conditions by using the histogram equalization algorithm on a single...

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Bibliographic Details
Published in:EURASIP journal on advances in signal processing 2010-01, Vol.2010 (1), p.628018-628018
Main Authors: Suh, Youngjoo, Kim, Hoirin
Format: Article
Language:English
Subjects:
Compensation
Methods
Speech
Studies
Online Access:Get full text
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Summary:We propose a new model adaptation method based on the histogram equalization technique for providing robustness in noisy environments. The trained acoustic mean models of a speech recognizer are adapted into environmentally matched conditions by using the histogram equalization algorithm on a single utterance basis. For more robust speech recognition in the heavily noisy conditions, trained acoustic covariance models are efficiently adapted by the signal-to-noise ratio-dependent linear interpolation between trained covariance models and utterance-level sample covariance models. Speech recognition experiments on both the digit-based Aurora2 task and the large vocabulary-based task showed that the proposed model adaptation approach provides significant performance improvements compared to the baseline speech recognizer trained on the clean speech data.
ISSN:1687-6172
1687-6180
1687-6180
DOI:10.1186/1687-6180-2010-628018