{"id":608625,"date":"2019-09-13T21:17:49","date_gmt":"2019-09-14T04:17:49","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=608625"},"modified":"2021-10-17T23:02:56","modified_gmt":"2021-10-18T06:02:56","slug":"dover-a-method-for-combining-diarization-outputs","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/dover-a-method-for-combining-diarization-outputs\/","title":{"rendered":"DOVER: A Method for Combining Diarization Outputs"},"content":{"rendered":"

Speech recognition and other natural language tasks have long benefited from voting-based algorithms as a method to aggregate outputs from several systems to achieve a higher accuracy than any of the individual systems. Diarization, the task of segmenting an audio stream into speaker-homogeneous and co-indexed regions, has so far not seen the benefit of this strategy because the structure of the task does not lend itself to a simple voting approach. This paper presents DOVER (diarization output voting error reduction), an algorithm for weighted voting among diarization hypotheses, in the spirit of the ROVER algorithm for combining speech recognition hypotheses. We evaluate the algorithm for diarization of meeting recordings with multiple microphones, and find that it consistently reduces diarization error rate over the average of results from individual channels, and often improves on the single best channel chosen by an oracle. The code is available here (opens in new tab)<\/span><\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"

Speech recognition and other natural language tasks have long benefited from voting-based algorithms as a method to aggregate outputs from several systems to achieve a higher accuracy than any of the individual systems. Diarization, the task of segmenting an audio stream into speaker-homogeneous and co-indexed regions, has so far not seen the benefit of this […]<\/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":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13556,13545],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-608625","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-research-area-human-language-technologies","msr-locale-en_us"],"msr_publishername":"IEEE","msr_edition":"","msr_affiliation":"","msr_published_date":"2019-12-14","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"IEEE","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"url","viewUrl":"false","id":"false","title":"https:\/\/arxiv.org\/pdf\/1909.08090.pdf","label_id":"243109","label":0}],"msr_related_uploader":"","msr_attachments":[{"id":608718,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2019\/09\/dover_asru2019.pdf"},{"id":608715,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2019\/09\/DOVER__A_Method_for_Combining_Diarization_Outputs__ASRU_2019_.pdf"},{"id":608628,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2019\/09\/DOVER__A_Method_for_Combining_Diarization_Outputs__ASRU_2019.pdf"}],"msr-author-ordering":[{"type":"user_nicename","value":"Andreas Stolcke","user_id":31054,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Andreas Stolcke"},{"type":"user_nicename","value":"Takuya Yoshioka","user_id":36278,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Takuya Yoshioka"}],"msr_impact_theme":[],"msr_research_lab":[199565],"msr_event":[],"msr_group":[664548,783091],"msr_project":[585154,171185],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":585154,"post_title":"Project Denmark","post_name":"project-denmark","post_type":"msr-project","post_date":"2019-05-09 13:13:15","post_modified":"2020-11-12 13:43:43","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/project-denmark\/","post_excerpt":"The goal of Project Denmark is to move beyond the need for traditional microphone arrays, such as those supported by Microsoft\u2019s Speech Devices SDK, to achieve high-quality capture of meeting conversations.","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/585154"}]}},{"ID":171185,"post_title":"Meeting Recognition and Understanding","post_name":"meeting-recognition-and-understanding","post_type":"msr-project","post_date":"2013-07-30 14:28:35","post_modified":"2023-08-12 21:11:41","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/meeting-recognition-and-understanding\/","post_excerpt":"In most organizations, staff spend many hours in meetings. This project addresses all levels of analysis and understanding, from speaker tracking and robust speech transcription to meaning extraction and summarization, with the goal of increasing productivity both during the meeting and after, for both participants and nonparticipants. The Meeting Recognition and Understanding project is a collection of online and offline spoken language understanding tasks. 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