{"id":151743,"date":"2004-05-01T00:00:00","date_gmt":"2004-05-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/refining-segmental-boundaries-for-tts-database-using-fine-contextual-dependent-boundary-models\/"},"modified":"2018-10-16T21:37:44","modified_gmt":"2018-10-17T04:37:44","slug":"refining-segmental-boundaries-for-tts-database-using-fine-contextual-dependent-boundary-models","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/refining-segmental-boundaries-for-tts-database-using-fine-contextual-dependent-boundary-models\/","title":{"rendered":"Refining Segmental Boundaries for TTS database Using Fine Contextual-Dependent Boundary Models"},"content":{"rendered":"<div class=\"asset-content\">\n<p>This paper proposed a post-refining method with fine contextual-dependent GMMs for the auto-segmentation task. A GMM trained with a super feature vector extracted from multiple evenly spaced frames near the boundary is suggested to describe the waveform evolution across a boundary. CART is used to cluster acoustically similar GMMs, so that the GMM for each leaf node is reliably trained by the limited manually labeled boundaries. An accuracy of 90% is thus achieved when only 250 manually labeled sentences are provided to train the refining models.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper proposed a post-refining method with fine contextual-dependent GMMs for the auto-segmentation task. A GMM trained with a super feature vector extracted from multiple evenly spaced frames near the boundary is suggested to describe the waveform evolution across a boundary. CART is used to cluster acoustically similar GMMs, so that the GMM for each [&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":"Institute of Electrical and Electronics Engineers, Inc.","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","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":"\u00a9 2004 IEEE. Personal use of this material is permitted. However, permission to reprint\/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.","msr_conference_name":"","msr_doi":"","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"Jianlai Zhou, Zhigang 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