{"id":166284,"date":"2001-01-01T00:00:00","date_gmt":"2001-01-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/on-line-unsupervised-adaptation-in-speaker-verification-confidence-based-updates-and-improved-parameter-estimation\/"},"modified":"2018-10-16T20:13:57","modified_gmt":"2018-10-17T03:13:57","slug":"on-line-unsupervised-adaptation-in-speaker-verification-confidence-based-updates-and-improved-parameter-estimation","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/on-line-unsupervised-adaptation-in-speaker-verification-confidence-based-updates-and-improved-parameter-estimation\/","title":{"rendered":"On-Line Unsupervised Adaptation in Speaker Verification: Confidence-Based Updates and Improved Parameter Estimation"},"content":{"rendered":"<div class=\"asset-content\">\n<p>This paper presents the second part of a new approach to on-line unsupervised adaptation in speaker verification. The new approach extends previous work in the literature by (1) improving performance on the enrollment handset-type when adapting on a different handset-type (e.g., improving performance on cellular when adapting on a landline office phone), (2) accomplishing this cross channel improvement without increasing the size of the speaker model after adaptation, (3) employing a count-based, parameter-dependent smoothing algorithm that emphasizes the use of mean parameters in the speaker models until sufficient adaptation data are present to accurately estimate variances, and (4) developing a new confidence-based adaptation update weight which minimizes the corrupting effects on the speaker models from impostor attacks. Experimental results show a 61% (rel.) overall reduction in EER using the new on-line adaptation approach even with a significant impostor attack rate, and a 24% improvement in EER due to the new confidence-based adaptation scheme for those speaker models corrupted by impostor utterances.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper presents the second part of a new approach to on-line unsupervised adaptation in speaker verification. The new approach extends previous work in the literature by (1) improving performance on the enrollment handset-type when adapting on a different handset-type (e.g., improving performance on cellular when adapting on a landline office phone), (2) accomplishing this [&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":null,"msr_publishername":"ISCA","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"ISCA Tutorial and Research Workshop (ITRW) on Adaptation Methods for Speech Recognition","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":"ISCA Tutorial and 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