{"id":151895,"date":"2005-09-01T00:00:00","date_gmt":"2005-09-01T00:00:00","guid":{"rendered":"https:\/\/newed.any0.dpdns.org\/en-us\/research\/msr-research-item\/hidden-conditional-random-fields-for-phone-classification\/"},"modified":"2018-10-16T19:55:32","modified_gmt":"2018-10-17T02:55:32","slug":"hidden-conditional-random-fields-for-phone-classification","status":"publish","type":"msr-research-item","link":"https:\/\/newed.any0.dpdns.org\/en-us\/research\/publication\/hidden-conditional-random-fields-for-phone-classification\/","title":{"rendered":"Hidden Conditional Random Fields for Phone Classification"},"content":{"rendered":"<div class=\"asset-content\">\n<p>In this paper, we show the novel application of hidden conditional random fields (HCRFs) \u2013 conditional random fields with hidden state sequences \u2013 for modeling speech. Hidden state sequences are critical for modeling the non-stationarity of speech signals. We show that HCRFs can easily be trained using the simple direct optimization technique of stochastic gradient descent. We present the results on the TIMIT phone classification task and show that HCRFs outperforms comparable ML and CML\/MMI trained HMMs. In fact, HCRF results on this task are the best single classifier results known to us. We note that the HCRF framework is easily extensible to recognition since it is a state and label sequence modeling technique. We also note that HCRFs have the ability to handle complex features without any change in training procedure.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper, we show the novel application of hidden conditional random fields (HCRFs) \u2013 conditional random fields with hidden state sequences \u2013 for modeling speech. Hidden state sequences are critical for modeling the non-stationarity of speech signals. We show that HCRFs can easily be trained using the simple direct optimization technique of stochastic gradient [&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":"jplatt"},{"type":"user_nicename","value":"aselag"},{"type":"user_nicename","value":"milindm"},{"type":"user_nicename","value":"alexac"}],"msr_publishername":"International Speech Communication Association","msr_publisher_other":"","msr_booktitle":"International Conference on Speech Communication and Technology","msr_chapter":"","msr_edition":"International Conference on Speech Communication and Technology","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 2004 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":"International Conference on Speech Communication and 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