{"id":167938,"date":"2015-04-01T00:00:00","date_gmt":"2015-04-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/multimodal-addressee-detection-in-multiparty-dialogue-systems\/"},"modified":"2018-10-16T20:02:14","modified_gmt":"2018-10-17T03:02:14","slug":"multimodal-addressee-detection-in-multiparty-dialogue-systems","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/multimodal-addressee-detection-in-multiparty-dialogue-systems\/","title":{"rendered":"Multimodal Addressee Detection in Multiparty Dialogue Systems"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Addressee detection answers the question, \u201cAre you talking to me?\u201d When multiple users interact with a dialogue system, it is important to know when a user is speaking to the computer and when he or she is speaking to another person. We approach this problem from a multimodal perspective, using lexical, acoustic, visual, dialog state, and beam-forming information. Using data from a multiparty dialogue system, we demonstrate the benefit of using multiple modalities over using a single modality. We also assess the relative importance of the various modalities in predicting the addressee. In our experiments, we find that acoustic features are by far the most important, that ASR and system-state information are useful, and that visual and beamforming features provide little additional benefit. Our study suggests that acoustic, lexical, and system state information are an effective, economical combination of modalities to use in addressee detection.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Addressee detection answers the question, \u201cAre you talking to me?\u201d When multiple users interact with a dialogue system, it is important to know when a user is speaking to the computer and when he or she is speaking to another person. We approach this problem from a multimodal perspective, using lexical, acoustic, visual, dialog state, [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13545],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-167938","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-human-language-technologies","msr-locale-en_us"],"msr_publishername":"IEEE - Institute of Electrical and Electronics Engineers","msr_edition":"Proc. 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