{"id":162745,"date":"2005-01-01T00:00:00","date_gmt":"2005-01-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/on-designing-and-evaluating-speech-event-detectors-2\/"},"modified":"2018-10-16T20:56:18","modified_gmt":"2018-10-17T03:56:18","slug":"on-designing-and-evaluating-speech-event-detectors-2","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/on-designing-and-evaluating-speech-event-detectors-2\/","title":{"rendered":"On Designing and Evaluating Speech Event Detectors"},"content":{"rendered":"<div class=\"asset-content\">\n<p>We study issues related to designing speech event detectors for<br \/>\nautomatic speech recognition. Event detection is a critical<br \/>\ncomponent of a recently proposed automatic speech attribute<br \/>\ntranscription (ASAT) paradigm for speech research. Similar to<br \/>\nkeyword spotting and non-keyword rejection, a good detector<br \/>\nneeds to effectively detect speech attributes of interest while<br \/>\nrejecting extraneous events. We compare frame and segment<br \/>\nbased detectors, study their properties in detecting manners of<br \/>\narticulation, and propose new performance measures. We test<br \/>\nthese detectors on the TIMIT database with several evaluation<br \/>\ncriteria. Our results indicate that segment based detectors<br \/>\noutperform frame based detectors in several key aspects of<br \/>\nspeech detector design. We also show that the performance<br \/>\ncan be significantly enhanced by incorporating discriminative<br \/>\ntraining into designing speech event detectors.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We study issues related to designing speech event detectors for automatic speech recognition. Event detection is a critical component of a recently proposed automatic speech attribute transcription (ASAT) paradigm for speech research. Similar to keyword spotting and non-keyword rejection, a good detector needs to effectively detect speech attributes of interest while rejecting extraneous events. We [&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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