{"id":163170,"date":"2012-09-01T00:00:00","date_gmt":"2012-09-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/learning-when-to-listen-detecting-system-addressed-speech-in-human-human-computer-dialog\/"},"modified":"2018-10-16T21:38:54","modified_gmt":"2018-10-17T04:38:54","slug":"learning-when-to-listen-detecting-system-addressed-speech-in-human-human-computer-dialog","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/learning-when-to-listen-detecting-system-addressed-speech-in-human-human-computer-dialog\/","title":{"rendered":"Learning When to Listen: Detecting System-Addressed Speech in Human-Human-Computer Dialog"},"content":{"rendered":"<div class=\"asset-content\">\n<p>New challenges arise for addressee detection when multiple people interact jointly with a spoken dialog system using unconstrained natural language. We study the problem of discriminating computer-directed from human-directed speech in a new corpus of human-human-computer (H-H-C) dialog, using lexical and prosodic features. The prosodic features use no word, context, or speaker information. Results with 19% WER speech recognition show improvements from lexical features (EER=23.1%) to prosodic features (EER=12.6%) to a combined model (EER=11.1%). Prosodic features also provide a 35% error reduction over a lexical model using true words (EER from 10.2% to 6.7%). Modeling energy contours with GMMs provides a particularly good prosodic model. While lexical models perform well for commands, they confuse free-form system-directed speech with human-human speech. Prosodic models dramatically reduce these confusions, implying that users change speaking style as they shift addressees (computer versus human) within a session. Overall results provide strong support for combining simple acoustic-prosodic models with lexical models to detect speaking style differences for this task.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>New challenges arise for addressee detection when multiple people interact jointly with a spoken dialog system using unconstrained natural language. We study the problem of discriminating computer-directed from human-directed speech in a new corpus of human-human-computer (H-H-C) dialog, using lexical and prosodic features. The prosodic features use no word, context, or speaker information. Results with [&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":"elshribe","user_id":"31734"},{"type":"user_nicename","value":"anstolck","user_id":"31054"},{"type":"user_nicename","value":"dilekha","user_id":"31630"},{"type":"user_nicename","value":"lheck","user_id":"32659"}],"msr_publishername":"ISCA - International Speech Communication Association","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":"334-337","msr_page_range_start":"334","msr_page_range_end":"337","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"Proc. 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