Towards automatic phonetic segmentation for TTS
Phonetic segmentation is an important step in the development of a concatenative TTS voice. This paper introduces a segmentation process consisting of two phases. First, forced alignment is performed using an HMM-GMM model. The resulting segmentation is then locally refined using an SVM based bounda...
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| creator | Rendel, A. Sorin, A. Hoory, R. Breen, A. |
| description | Phonetic segmentation is an important step in the development of a concatenative TTS voice. This paper introduces a segmentation process consisting of two phases. First, forced alignment is performed using an HMM-GMM model. The resulting segmentation is then locally refined using an SVM based boundary model. Both the models are derived from multi-speaker data using a speaker adaptive training procedure. Evaluation results are obtained on the TIMIT corpus and on a proprietary single-speaker TTS corpus. |
| doi_str_mv | 10.1109/ICASSP.2012.6288926 |
| format | Conference Proceeding |
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This paper introduces a segmentation process consisting of two phases. First, forced alignment is performed using an HMM-GMM model. The resulting segmentation is then locally refined using an SVM based boundary model. Both the models are derived from multi-speaker data using a speaker adaptive training procedure. Evaluation results are obtained on the TIMIT corpus and on a proprietary single-speaker TTS corpus.</description><subject>Accuracy</subject><subject>Acoustics</subject><subject>Adaptation models</subject><subject>Data models</subject><subject>Hidden Markov models</subject><subject>Phoneme alignment</subject><subject>Phonetic segmentation</subject><subject>Text to speech</subject><subject>Training</subject><subject>Vectors</subject><issn>1520-6149</issn><issn>2379-190X</issn><isbn>1467300454</isbn><isbn>9781467300452</isbn><isbn>9781467300469</isbn><isbn>1467300446</isbn><isbn>9781467300445</isbn><isbn>1467300462</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1T9tKw1AQPN7AWPMFfckPJN3dc8s-StEqFFpIBN_KSXKiEZOUJCL-vRHrvswwwwyzQiwREkTg1dP6Lsv2CQFSYihNmcyZCNmmqIyVAMrwuQhIWo6R4eVC3PwbWl2KADVBbFDxtQjH8R3mm6MgTSBWef_lhmqM3OfUt25qyuj41nf-l4z-tfXdNIt9F9X9EOV5diuuavcx-vCEC_H8cJ-vH-PtbjOv3MYNWj3F5OaNhawkkbZGSdDMlqH0pa4dkUKrIGUGh0VFpkBKVeVkWXss0HhDciGWf72N9_5wHJrWDd-H0-_yB743R60</recordid><startdate>201203</startdate><enddate>201203</enddate><creator>Rendel, A.</creator><creator>Sorin, A.</creator><creator>Hoory, R.</creator><creator>Breen, A.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>201203</creationdate><title>Towards automatic phonetic segmentation for TTS</title><author>Rendel, A. ; Sorin, A. ; Hoory, R. ; Breen, A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-2a892b3d322576430599790cec5fa22417408990a1bd26b1284da3cfe1b16e623</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Accuracy</topic><topic>Acoustics</topic><topic>Adaptation models</topic><topic>Data models</topic><topic>Hidden Markov models</topic><topic>Phoneme alignment</topic><topic>Phonetic segmentation</topic><topic>Text to speech</topic><topic>Training</topic><topic>Vectors</topic><toplevel>online_resources</toplevel><creatorcontrib>Rendel, A.</creatorcontrib><creatorcontrib>Sorin, A.</creatorcontrib><creatorcontrib>Hoory, R.</creatorcontrib><creatorcontrib>Breen, A.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Rendel, A.</au><au>Sorin, A.</au><au>Hoory, R.</au><au>Breen, A.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Towards automatic phonetic segmentation for TTS</atitle><btitle>2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)</btitle><stitle>ICASSP</stitle><date>2012-03</date><risdate>2012</risdate><spage>4533</spage><epage>4536</epage><pages>4533-4536</pages><issn>1520-6149</issn><eissn>2379-190X</eissn><isbn>1467300454</isbn><isbn>9781467300452</isbn><eisbn>9781467300469</eisbn><eisbn>1467300446</eisbn><eisbn>9781467300445</eisbn><eisbn>1467300462</eisbn><abstract>Phonetic segmentation is an important step in the development of a concatenative TTS voice. This paper introduces a segmentation process consisting of two phases. First, forced alignment is performed using an HMM-GMM model. The resulting segmentation is then locally refined using an SVM based boundary model. Both the models are derived from multi-speaker data using a speaker adaptive training procedure. Evaluation results are obtained on the TIMIT corpus and on a proprietary single-speaker TTS corpus.</abstract><pub>IEEE</pub><doi>10.1109/ICASSP.2012.6288926</doi><tpages>4</tpages></addata></record> |
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| ispartof | 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012, p.4533-4536 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Accuracy Acoustics Adaptation models Data models Hidden Markov models Phoneme alignment Phonetic segmentation Text to speech Training Vectors |
| title | Towards automatic phonetic segmentation for TTS |
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