{"id":162746,"date":"2009-01-01T00:00:00","date_gmt":"2009-01-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/a-study-on-soft-margin-estimation-of-linear-regression-parameters-for-speaker-adaptation\/"},"modified":"2018-10-16T20:56:30","modified_gmt":"2018-10-17T03:56:30","slug":"a-study-on-soft-margin-estimation-of-linear-regression-parameters-for-speaker-adaptation","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/a-study-on-soft-margin-estimation-of-linear-regression-parameters-for-speaker-adaptation\/","title":{"rendered":"A study on soft margin estimation of linear regression parameters for speaker adaptation"},"content":{"rendered":"<div class=\"asset-content\">\n<p>We formulate a framework for soft margin estimation-based<br \/>\nlinear regression (SMELR) and apply it to supervised speaker<br \/>\nadaptation. Enhanced separation capability and increased discriminative<br \/>\nability are two key properties in margin-based discriminative<br \/>\ntraining. For the adaptation process to be able to<br \/>\nflexibly utilize any amount of data, we also propose a novel interpolation<br \/>\nscheme to linearly combine the speaker independent<br \/>\n(SI) and speaker adaptive SMELR (SMELR\/SA) models. The<br \/>\ntwo proposed SMELR algorithms were evaluated on a Japanese<br \/>\nlarge vocabulary continuous speech recognition task. Both the<br \/>\nSMELR and interpolated SI+SMELR\/SA techniques showed<br \/>\nimproved speech adaptation performance in comparison with<br \/>\nthe well-known maximum likelihood linear regression (MLLR)<br \/>\nmethod. We also found that the interpolation framework works<br \/>\neven more effectively than SMELR when the amount of adaptation<br \/>\ndata is relatively small.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We formulate a framework for soft margin estimation-based linear regression (SMELR) and apply it to supervised speaker adaptation. Enhanced separation capability and increased discriminative ability are two key properties in margin-based discriminative training. For the adaptation process to be able to flexibly utilize any amount of data, we also propose a novel interpolation scheme to [&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":"","msr_chapter":"","msr_edition":"Proc. Interspeech","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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