{"id":158895,"date":"2010-03-01T00:00:00","date_gmt":"2010-03-01T00:00:00","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/msr-research-item\/context-dependent-phonetic-string-edit-distance-for-automatic-speech-recognition\/"},"modified":"2018-10-16T21:03:51","modified_gmt":"2018-10-17T04:03:51","slug":"context-dependent-phonetic-string-edit-distance-for-automatic-speech-recognition","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/context-dependent-phonetic-string-edit-distance-for-automatic-speech-recognition\/","title":{"rendered":"Context Dependent Phonetic String Edit Distance for Automatic Speech Recognition"},"content":{"rendered":"<div class=\"asset-content\">\n<p>An automatic speech recognition system searches for the word transcription with the highest overall score for a given acoustic observation sequence. This overall score is typically a weighted combination of a language model score and an acoustic model score. We propose including a third score, which measures the similarity of the word transcription&#8217;s pronunciation to the output of a less constrained phonetic recognizer. We show how this phonetic string edit distance can be learned from data, and that including context in the model is essential for good performance. We demonstrate improved accuracy on a business search task.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>An automatic speech recognition system searches for the word transcription with the highest overall score for a given acoustic observation sequence. This overall score is typically a weighted combination of a language model score and an acoustic model score. We propose including a third score, which measures the similarity of the word transcription&#8217;s pronunciation 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":"jdroppo"},{"type":"user_nicename","value":"alexac"}],"msr_publishername":"IEEE","msr_publisher_other":"","msr_booktitle":"ICASSP","msr_chapter":"","msr_edition":"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":"\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 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