{"id":159898,"date":"2010-01-01T00:00:00","date_gmt":"2010-01-01T00:00:00","guid":{"rendered":"https:\/\/newed.any0.dpdns.org\/en-us\/research\/msr-research-item\/hmm-based-pseudo-clean-speech-synthesis-for-splice-algorithm\/"},"modified":"2018-10-16T20:00:48","modified_gmt":"2018-10-17T03:00:48","slug":"hmm-based-pseudo-clean-speech-synthesis-for-splice-algorithm","status":"publish","type":"msr-research-item","link":"https:\/\/newed.any0.dpdns.org\/en-us\/research\/publication\/hmm-based-pseudo-clean-speech-synthesis-for-splice-algorithm\/","title":{"rendered":"HMM-Based Pseudo-Clean Speech Synthesis for SPLICE Algorithm"},"content":{"rendered":"<div class=\"asset-content\">\n<p>In this paper, we present a novel approach to relax the constraint of stereo-data which is needed in a series of algorithms for noise-robust speech recognition. As a demonstration in SPLICE algorithm, we generate the pseudo-clean features to replace the ideal clean features from one of the stereo channels, by using HMM-based speech synthesis. Experimental results on aurora2 database show that the performance of our approach is comparable with that of SPLICE. Further improvements are achieved by concatenating a bias adaptation algorithm to handle unknown environments. Relative word error rate reductions of 66% and 24% are achieved over the baseline systems in the clean-training and multi-training conditions, respectively.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper, we present a novel approach to relax the constraint of stereo-data which is needed in a series of algorithms for noise-robust speech recognition. As a demonstration in SPLICE algorithm, we generate the pseudo-clean features to replace the ideal clean features from one of the stereo channels, by using HMM-based speech synthesis. Experimental [&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":null,"msr_publishername":"IEEE","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"Proc. of ICASSP 2010","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":"\u00a9 2008 IEEE. Personal use of this material is permitted. However, permission to reprint\/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.http:\/\/www.ieee.org\/","msr_conference_name":"Proc. of ICASSP 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