{"id":167804,"date":"2000-01-01T00:00:00","date_gmt":"2000-01-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/foldering-voicemail-messages-by-caller-using-text-independent-speaker-recognition\/"},"modified":"2019-03-02T13:02:35","modified_gmt":"2019-03-02T21:02:35","slug":"foldering-voicemail-messages-by-caller-using-text-independent-speaker-recognition","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/foldering-voicemail-messages-by-caller-using-text-independent-speaker-recognition\/","title":{"rendered":"Foldering voicemail messages by caller using text independent speaker recognition."},"content":{"rendered":"<p><span style=\"font-family: arial,helvetica,verdana\">The ability to automatically scan voicemail messages for content and caller identity cues would be a useful service. This paper describes a system which automatically files voicemail messages into caller folders using text independent speaker recognition techniques. Callers are represented by Gaussian mixture models (GMM&#8217;s). The speech for an incoming message is processed and scored against caller models created for a subscriber. A message whose matching score exceeds a threshold is filed in the matching caller folder; otherwise it is tagged as &#8220;unknown&#8221;. The subscriber has the ability to listen to an &#8220;unknown&#8221; message and file it in the proper folder, if it exists, or create a new folder, if it does not. Such subscriber labelled messages are used to train and adapt caller models. The system has been evaluated on a database of voicemail messages collected at AT&T Labs. A set of 20 callers from this database is designated as &#8220;ingroup&#8221;. Each of these callers has recorded at least 20 messages totalling 10 or more minutes in duration. A distinct set of 220 messages, each from a dierent caller, are designated as &#8220;outgroup&#8221;. Representative performance figures with threshold parameters set to ensure that outgroup acceptance is low compared with ingroup rejection are the following. The average ingroup message rejection rate is 11.0% and the average ingroup message confusion rate (matching the wrong caller) is 1.0%, while the average outgroup message accept rate is 2.7%. <\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The ability to automatically scan voicemail messages for content and caller identity cues would be a useful service. This paper describes a system which automatically files voicemail messages into caller folders using text independent speaker recognition techniques. Callers are represented by Gaussian mixture models (GMM&#8217;s). The speech for an incoming message is processed and scored [&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-167804","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-human-language-technologies","msr-locale-en_us"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2000-10-16","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":"","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":0,"msr_main_download":"","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"url","viewUrl":"false","id":"false","title":"https:\/\/www.isca-speech.org\/archive\/icslp_2000\/i00_2474.html","label_id":"243109","label":0}],"msr_related_uploader":"","msr_attachments":[],"msr-author-ordering":[{"type":"text","value":"Aaron E. Rosenberg","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Sarangarajan Parthasarathy","user_id":33525,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Sarangarajan Parthasarathy"},{"type":"text","value":"Julia Hirschberg","user_id":0,"rest_url":false},{"type":"text","value":"Stephen Whittaker","user_id":0,"rest_url":false}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[],"msr_project":[],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/167804"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-research-item"}],"version-history":[{"count":2,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/167804\/revisions"}],"predecessor-version":[{"id":570918,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/167804\/revisions\/570918"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=167804"}],"wp:term":[{"taxonomy":"msr-content-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-content-type?post=167804"},{"taxonomy":"msr-research-highlight","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-highlight?post=167804"},{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=167804"},{"taxonomy":"msr-publication-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-publication-type?post=167804"},{"taxonomy":"msr-product-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-product-type?post=167804"},{"taxonomy":"msr-focus-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-focus-area?post=167804"},{"taxonomy":"msr-platform","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-platform?post=167804"},{"taxonomy":"msr-download-source","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-download-source?post=167804"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=167804"},{"taxonomy":"msr-field-of-study","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-field-of-study?post=167804"},{"taxonomy":"msr-conference","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-conference?post=167804"},{"taxonomy":"msr-journal","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-journal?post=167804"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=167804"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=167804"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}