Statistical approach to enhancing esophageal speech based on Gaussian mixture models
This paper presents a novel method of enhancing esophageal speech using statistical voice conversion. Esophageal speech is one of the alternative speaking methods for laryngectomees. Although it doesn't require any external devices, generated voices sound unnatural. To improve the intelligibili...
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| creator | Doi, Hironori Nakamura, Keigo Toda, Tomoki Saruwatari, Hiroshi Shikano, Kiyohiro |
| description | This paper presents a novel method of enhancing esophageal speech using statistical voice conversion. Esophageal speech is one of the alternative speaking methods for laryngectomees. Although it doesn't require any external devices, generated voices sound unnatural. To improve the intelligibility and naturalness of esophageal speech, we propose a voice conversion method from esophageal speech into normal speech. A spectral parameter and excitation parameters of target normal speech are separately estimated from a spectral parameter of the esophageal speech based on Gaussian mixture models. The experimental results demonstrate that the proposed method yields significant improvements in intelligibility and naturalness. We also apply one-to-many eigenvoice conversion to esophageal speech enhancement for flexibly controlling enhanced voice quality. |
| doi_str_mv | 10.1109/ICASSP.2010.5495676 |
| format | Conference Proceeding |
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Esophageal speech is one of the alternative speaking methods for laryngectomees. Although it doesn't require any external devices, generated voices sound unnatural. To improve the intelligibility and naturalness of esophageal speech, we propose a voice conversion method from esophageal speech into normal speech. A spectral parameter and excitation parameters of target normal speech are separately estimated from a spectral parameter of the esophageal speech based on Gaussian mixture models. The experimental results demonstrate that the proposed method yields significant improvements in intelligibility and naturalness. We also apply one-to-many eigenvoice conversion to esophageal speech enhancement for flexibly controlling enhanced voice quality.</description><identifier>ISSN: 1520-6149</identifier><identifier>ISBN: 9781424442959</identifier><identifier>ISBN: 1424442958</identifier><identifier>EISSN: 2379-190X</identifier><identifier>EISBN: 9781424442966</identifier><identifier>EISBN: 1424442966</identifier><identifier>DOI: 10.1109/ICASSP.2010.5495676</identifier><language>eng</language><publisher>IEEE</publisher><subject>Acoustic noise ; Degradation ; eigenvoice conversion ; esophageal speech ; Esophagus ; Information science ; laryngectomees ; Loudspeakers ; Noise generators ; Speech analysis ; Speech enhancement ; Speech processing ; Virtual colonoscopy ; voice conversion</subject><ispartof>2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010, p.4250-4253</ispartof><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c298t-12d4ffa8089693689ab05b53935f9f1aecbd51f233ff0679995c27529646d1853</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5495676$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2056,27923,54918</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5495676$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Doi, Hironori</creatorcontrib><creatorcontrib>Nakamura, Keigo</creatorcontrib><creatorcontrib>Toda, Tomoki</creatorcontrib><creatorcontrib>Saruwatari, Hiroshi</creatorcontrib><creatorcontrib>Shikano, Kiyohiro</creatorcontrib><title>Statistical approach to enhancing esophageal speech based on Gaussian mixture models</title><title>2010 IEEE International Conference on Acoustics, Speech and Signal Processing</title><addtitle>ICASSP</addtitle><description>This paper presents a novel method of enhancing esophageal speech using statistical voice conversion. Esophageal speech is one of the alternative speaking methods for laryngectomees. Although it doesn't require any external devices, generated voices sound unnatural. To improve the intelligibility and naturalness of esophageal speech, we propose a voice conversion method from esophageal speech into normal speech. A spectral parameter and excitation parameters of target normal speech are separately estimated from a spectral parameter of the esophageal speech based on Gaussian mixture models. The experimental results demonstrate that the proposed method yields significant improvements in intelligibility and naturalness. We also apply one-to-many eigenvoice conversion to esophageal speech enhancement for flexibly controlling enhanced voice quality.</description><subject>Acoustic noise</subject><subject>Degradation</subject><subject>eigenvoice conversion</subject><subject>esophageal speech</subject><subject>Esophagus</subject><subject>Information science</subject><subject>laryngectomees</subject><subject>Loudspeakers</subject><subject>Noise generators</subject><subject>Speech analysis</subject><subject>Speech enhancement</subject><subject>Speech processing</subject><subject>Virtual colonoscopy</subject><subject>voice conversion</subject><issn>1520-6149</issn><issn>2379-190X</issn><isbn>9781424442959</isbn><isbn>1424442958</isbn><isbn>9781424442966</isbn><isbn>1424442966</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2010</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpVkN1Kw0AUhNc_sNY-QW_2BVL3P3supWgVCgqp4F05Sc62K20Ssino2xuwN14NzAwD3zA2l2IhpYCH1-VjUbwvlBgNa8C63F2wGeReGmWMUeDcJZsonUMmQXxe_cssXLOJtEpkThq4ZXcpfQkhfG78hG2KAYeYhljhgWPX9S1Wez60nJo9NlVsdpxS2-1xR2MhdURjXGKimrcNX-EppYgNP8bv4dQTP7Y1HdI9uwl4SDQ765R9PD9tli_Z-m01oqyzSoEfMqlqEwJ64cGBdh6wFLa0GrQNECRSVdZWBqV1CMLlAGArlduR1rhaequnbP63G4lo2_XxiP3P9nyQ_gWrdlYS</recordid><startdate>201003</startdate><enddate>201003</enddate><creator>Doi, Hironori</creator><creator>Nakamura, Keigo</creator><creator>Toda, Tomoki</creator><creator>Saruwatari, Hiroshi</creator><creator>Shikano, Kiyohiro</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>201003</creationdate><title>Statistical approach to enhancing esophageal speech based on Gaussian mixture models</title><author>Doi, Hironori ; Nakamura, Keigo ; Toda, Tomoki ; Saruwatari, Hiroshi ; Shikano, Kiyohiro</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c298t-12d4ffa8089693689ab05b53935f9f1aecbd51f233ff0679995c27529646d1853</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Acoustic noise</topic><topic>Degradation</topic><topic>eigenvoice conversion</topic><topic>esophageal speech</topic><topic>Esophagus</topic><topic>Information science</topic><topic>laryngectomees</topic><topic>Loudspeakers</topic><topic>Noise generators</topic><topic>Speech analysis</topic><topic>Speech enhancement</topic><topic>Speech processing</topic><topic>Virtual colonoscopy</topic><topic>voice conversion</topic><toplevel>online_resources</toplevel><creatorcontrib>Doi, Hironori</creatorcontrib><creatorcontrib>Nakamura, Keigo</creatorcontrib><creatorcontrib>Toda, Tomoki</creatorcontrib><creatorcontrib>Saruwatari, Hiroshi</creatorcontrib><creatorcontrib>Shikano, Kiyohiro</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>Doi, Hironori</au><au>Nakamura, Keigo</au><au>Toda, Tomoki</au><au>Saruwatari, Hiroshi</au><au>Shikano, Kiyohiro</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Statistical approach to enhancing esophageal speech based on Gaussian mixture models</atitle><btitle>2010 IEEE International Conference on Acoustics, Speech and Signal Processing</btitle><stitle>ICASSP</stitle><date>2010-03</date><risdate>2010</risdate><spage>4250</spage><epage>4253</epage><pages>4250-4253</pages><issn>1520-6149</issn><eissn>2379-190X</eissn><isbn>9781424442959</isbn><isbn>1424442958</isbn><eisbn>9781424442966</eisbn><eisbn>1424442966</eisbn><abstract>This paper presents a novel method of enhancing esophageal speech using statistical voice conversion. Esophageal speech is one of the alternative speaking methods for laryngectomees. Although it doesn't require any external devices, generated voices sound unnatural. To improve the intelligibility and naturalness of esophageal speech, we propose a voice conversion method from esophageal speech into normal speech. A spectral parameter and excitation parameters of target normal speech are separately estimated from a spectral parameter of the esophageal speech based on Gaussian mixture models. The experimental results demonstrate that the proposed method yields significant improvements in intelligibility and naturalness. We also apply one-to-many eigenvoice conversion to esophageal speech enhancement for flexibly controlling enhanced voice quality.</abstract><pub>IEEE</pub><doi>10.1109/ICASSP.2010.5495676</doi><tpages>4</tpages><oa>free_for_read</oa></addata></record> |
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| ispartof | 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010, p.4250-4253 |
| issn | 1520-6149 2379-190X |
| language | eng |
| recordid | cdi_ieee_primary_5495676 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Acoustic noise Degradation eigenvoice conversion esophageal speech Esophagus Information science laryngectomees Loudspeakers Noise generators Speech analysis Speech enhancement Speech processing Virtual colonoscopy voice conversion |
| title | Statistical approach to enhancing esophageal speech based on Gaussian mixture models |
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