{"id":162732,"date":"2007-01-01T00:00:00","date_gmt":"2007-01-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/soft-margin-feature-extraction-for-automatic-speech-recognition\/"},"modified":"2018-10-16T20:53:51","modified_gmt":"2018-10-17T03:53:51","slug":"soft-margin-feature-extraction-for-automatic-speech-recognition","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/soft-margin-feature-extraction-for-automatic-speech-recognition\/","title":{"rendered":"Soft Margin Feature Extraction for Automatic Speech Recognition"},"content":{"rendered":"<div class=\"asset-content\">\n<p>We propose a new discriminative learning framework, called<br \/>\nsoft margin feature extraction (SMFE), for jointly optimizing<br \/>\nthe parameters of transformation matrix for feature extraction<br \/>\nand of hidden Markov models (HMMs) for acoustic<br \/>\nmodeling. SMFE extends our previous work of soft margin<br \/>\nestimation (SME) to feature extraction. Tested on the<br \/>\nTIDIGITS connected digit recognition task, the proposed<br \/>\napproach achieves a string accuracy of 99.61%, much better<br \/>\nthan our previously reported SME results. To our knowledge,<br \/>\nthis is the first study on applying the margin-based method in<br \/>\njoint optimization of feature extraction and acoustic modeling.<br \/>\nThe excellent performance of SMFE demonstrates the success<br \/>\nof soft margin based method, which targets to obtain both<br \/>\nhigh accuracy and good model generalization.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We propose a new discriminative learning framework, called soft margin feature extraction (SMFE), for jointly optimizing the parameters of transformation matrix for feature extraction and of hidden Markov models (HMMs) for acoustic modeling. SMFE extends our previous work of soft margin estimation (SME) to feature extraction. Tested on the TIDIGITS connected digit recognition task, the [&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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