{"id":151777,"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\/segmental-tonal-modeling-for-phone-set-design-in-mandarin-lvcsr\/"},"modified":"2018-12-26T13:08:42","modified_gmt":"2018-12-26T21:08:42","slug":"segmental-tonal-modeling-for-phone-set-design-in-mandarin-lvcsr","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/segmental-tonal-modeling-for-phone-set-design-in-mandarin-lvcsr\/","title":{"rendered":"Segmental Tonal Modeling for Phone Set Design in Mandarin LVCSR"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Modeling units play a very important role in state-of-art speech recognition systems. The design and selection of them will directly impact the performance of final speech recognition engine. As a tonal language, Mandarin\u2019s modeling units are more special for the tonal processing. In this paper, after fully investigating several dominant modeling strategies, we propose a new phone set design strategy for Mandarin, called segmental tonal modeling. Instead of modeling tone types directly, we realized them implicitly and jointly by two segments, which both carry tonal information. Both HTK and SAPI based experiments confirmed that such method is very efficient. In addition to improving the accuracy by 9~23%, it greatly reduces the decoding time by 30~45%. Given the similar decoding speed, new phone set configuration can reduce the error rate by relatively 35%.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Modeling units play a very important role in state-of-art speech recognition systems. The design and selection of them will directly impact the performance of final speech recognition engine. As a tonal language, Mandarin\u2019s modeling units are more special for the tonal processing. In this paper, after fully investigating several dominant modeling strategies, we propose a [&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":"2004 IEEE International Conference on Acoustics, Speech, and Signal Processing","msr_doi":"","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"Jianlai Zhou, Terry 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