{"id":158448,"date":"2009-09-01T00:00:00","date_gmt":"2009-09-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/maximum-mutual-information-multi-phone-units-in-direct-modeling-2\/"},"modified":"2018-10-16T20:20:51","modified_gmt":"2018-10-17T03:20:51","slug":"maximum-mutual-information-multi-phone-units-in-direct-modeling-2","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/maximum-mutual-information-multi-phone-units-in-direct-modeling-2\/","title":{"rendered":"Maximum Mutual Information Multi-phone Units in Direct Modeling"},"content":{"rendered":"<div class=\"asset-content\">\n<p>This paper introduces a class of discriminative features for use in maximum entropy speech recognition models. The features we propose are acoustic detectors for discriminatively determined multi-phone units. The multi-phone units are found by computing the mutual information between the phonetic subsequences that occur in the training lexicon, and the word labels. This quantity is a function of an error model governing our ability to detect phone sequences accurately (an otherwise informative sequence which cannot be reliably detected is not so useful). We show how to compute this mutual information quantity under a class of error models efficiently, in one pass over the data, for all phonetic sub-sequences in the training data. After this computation, detectors are created for a subset of highly informative units. We then define two novel classes of features based on these units: associative and transductive. Incorporating these features in a maximum entropy based direct model for Voice-Search outperforms the baseline by 24% in sentence error rate.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper introduces a class of discriminative features for use in maximum entropy speech recognition models. The features we propose are acoustic detectors for discriminatively determined multi-phone units. The multi-phone units are found by computing the mutual information between the phonetic subsequences that occur in the training lexicon, and the word labels. This quantity is [&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":"gzweig"},{"type":"user_nicename","value":"panguyen"}],"msr_publishername":"International Speech Communication Association","msr_publisher_other":"","msr_booktitle":"Interspeech 2009","msr_chapter":"","msr_edition":"Interspeech 2009","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 2007 ISCA. 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 ISCA and\/or the author.","msr_conference_name":"Interspeech 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