{"id":162728,"date":"2006-01-01T00:00:00","date_gmt":"2006-01-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/a-study-on-lattice-rescoring-with-knowledge-scores-for-automatic-speech-recognition\/"},"modified":"2018-10-16T20:52:58","modified_gmt":"2018-10-17T03:52:58","slug":"a-study-on-lattice-rescoring-with-knowledge-scores-for-automatic-speech-recognition","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/a-study-on-lattice-rescoring-with-knowledge-scores-for-automatic-speech-recognition\/","title":{"rendered":"A study on lattice rescoring with knowledge scores for automatic speech recognition"},"content":{"rendered":"<div class=\"asset-content\">\n<p>We study lattice rescoring with knowledge scores for automatic<br \/>\nspeech recognition. Frame-based log likelihood ratio is adopted as<br \/>\na score measure of the goodness-of-fit between a speech segment<br \/>\nand the knowledge sources. We evaluate our approach in two different<br \/>\napplications: phone recognition, and connected digit continuous<br \/>\nrecognition. By incorporating knowledge scores obtained<br \/>\nfrom 15 attribute detectors for place and manner of articulation,<br \/>\nwe reduced phone error rate from 40.52% to 35.16% using monophone<br \/>\nmodels. The error rate can be further reduced to 33.42% for<br \/>\ntriphone models. The same lattice rescoring algorithm is extended<br \/>\nto connected digit recognition using the TIDIGITS database, and<br \/>\nwithout using any digit-specific training data. We observed the<br \/>\ndigit error rate can be effectively reduced to 4.03% from 4.54%<br \/>\nwhich was obtained with the conventional Viterbi decoding algorithm<br \/>\nwith no knowledge scores.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We study lattice rescoring with knowledge scores for automatic speech recognition. Frame-based log likelihood ratio is adopted as a score measure of the goodness-of-fit between a speech segment and the knowledge sources. We evaluate our approach in two different applications: phone recognition, and connected digit continuous recognition. By incorporating knowledge scores obtained from 15 attribute [&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":"Proc. Interspeech","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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