{"id":162749,"date":"2011-01-01T00:00:00","date_gmt":"2011-01-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/maximum-likelihood-adaptation-of-histogram-equalization-with-constraint-for-robust-speech-recognition\/"},"modified":"2018-10-16T20:56:55","modified_gmt":"2018-10-17T03:56:55","slug":"maximum-likelihood-adaptation-of-histogram-equalization-with-constraint-for-robust-speech-recognition","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/maximum-likelihood-adaptation-of-histogram-equalization-with-constraint-for-robust-speech-recognition\/","title":{"rendered":"Maximum likelihood adaptation of histogram equalization with constraint for robust speech recognition"},"content":{"rendered":"<div class=\"asset-content\">\n<p>In this paper, we propose a novel feature space adaptation technique<br \/>\nto improve the robustness of speech recognition in noisy environ-<br \/>\nments. Histogram equalization (HEQ) is an effective technique for<br \/>\nimproving robustness by reducing the difference between clean and<br \/>\nnoisy features. A weakness of HEQ is that it does not take into ac-<br \/>\ncount acoustic model, resulting in possible mismatch between HEQ-<br \/>\nprocessed features and the acoustic model. In this paper, we propose<br \/>\nto adapt HEQ to maximize the likelihood of HEQ-processed features<br \/>\non the acoustic model, with a constraint on the parameters of HEQ.<br \/>\nIn addition, we use a Gaussian mixture model (GMM) to represent<br \/>\nthe clean feature space rather than using the acoustic model itself,<br \/>\nand this results in both simpler implementation and better results.<br \/>\nExperimental results show that HEQ with adaptation reduces word<br \/>\nerror rate by 7.5% and 5.7% respectively on Aurora-2 and Auroar-4<br \/>\ntasks over the HEQ baseline without adaptation.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper, we propose a novel feature space adaptation technique to improve the robustness of speech recognition in noisy environ- ments. Histogram equalization (HEQ) is an effective technique for improving robustness by reducing the difference between clean and noisy features. A weakness of HEQ is that it does not take into ac- count acoustic [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"user_nicename","value":"jinyli"}],"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"Proc. ICASSP","msr_chapter":"","msr_edition":"Proc. ICASSP","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"Proc. 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