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...
Main Authors: | , |
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Format: | Article |
Language: | English |
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SpringerOpen
2010-01-01
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Series: | EURASIP Journal on Advances in Signal Processing |
Online Access: | http://dx.doi.org/10.1155/2010/628018 |
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author | Hoirin Kim Youngjoo Suh |
author_facet | Hoirin Kim Youngjoo Suh |
author_sort | Hoirin Kim |
collection | DOAJ |
description | 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. |
first_indexed | 2024-04-12T14:42:41Z |
format | Article |
id | doaj.art-99381377e4ce4e02af071ea58cf2d204 |
institution | Directory Open Access Journal |
issn | 1687-6172 1687-6180 |
language | English |
last_indexed | 2024-04-12T14:42:41Z |
publishDate | 2010-01-01 |
publisher | SpringerOpen |
record_format | Article |
series | EURASIP Journal on Advances in Signal Processing |
spelling | doaj.art-99381377e4ce4e02af071ea58cf2d2042022-12-22T03:28:46ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61721687-61802010-01-01201010.1155/2010/628018Histogram Equalization to Model Adaptation for Robust Speech RecognitionHoirin KimYoungjoo SuhWe 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.http://dx.doi.org/10.1155/2010/628018 |
spellingShingle | Hoirin Kim Youngjoo Suh Histogram Equalization to Model Adaptation for Robust Speech Recognition EURASIP Journal on Advances in Signal Processing |
title | Histogram Equalization to Model Adaptation for Robust Speech Recognition |
title_full | Histogram Equalization to Model Adaptation for Robust Speech Recognition |
title_fullStr | Histogram Equalization to Model Adaptation for Robust Speech Recognition |
title_full_unstemmed | Histogram Equalization to Model Adaptation for Robust Speech Recognition |
title_short | Histogram Equalization to Model Adaptation for Robust Speech Recognition |
title_sort | histogram equalization to model adaptation for robust speech recognition |
url | http://dx.doi.org/10.1155/2010/628018 |
work_keys_str_mv | AT hoirinkim histogramequalizationtomodeladaptationforrobustspeechrecognition AT youngjoosuh histogramequalizationtomodeladaptationforrobustspeechrecognition |