Towards General-Purpose Text-Instruction-Guided Voice Conversion
This paper introduces a novel voice conversion (VC) model, guided by text instructions such as "articulate slowly with a deep tone" or "speak in a cheerful boyish voice". Unlike traditional methods that rely on reference utterances to determine the attributes of the converted spe...
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creator | Kuan, Chun-Yi Li, Chen An Hsu, Tsu-Yuan Lin, Tse-Yang Chung, Ho-Lam Chang, Kai-Wei Chang, Shuo-yiin Lee, Hung-yi |
description | This paper introduces a novel voice conversion (VC) model, guided by text
instructions such as "articulate slowly with a deep tone" or "speak in a
cheerful boyish voice". Unlike traditional methods that rely on reference
utterances to determine the attributes of the converted speech, our model adds
versatility and specificity to voice conversion. The proposed VC model is a
neural codec language model which processes a sequence of discrete codes,
resulting in the code sequence of converted speech. It utilizes text
instructions as style prompts to modify the prosody and emotional information
of the given speech. In contrast to previous approaches, which often rely on
employing separate encoders like prosody and content encoders to handle
different aspects of the source speech, our model handles various information
of speech in an end-to-end manner. Experiments have demonstrated the impressive
capabilities of our model in comprehending instructions and delivering
reasonable results. |
doi_str_mv | 10.48550/arxiv.2309.14324 |
format | Article |
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instructions such as "articulate slowly with a deep tone" or "speak in a
cheerful boyish voice". Unlike traditional methods that rely on reference
utterances to determine the attributes of the converted speech, our model adds
versatility and specificity to voice conversion. The proposed VC model is a
neural codec language model which processes a sequence of discrete codes,
resulting in the code sequence of converted speech. It utilizes text
instructions as style prompts to modify the prosody and emotional information
of the given speech. In contrast to previous approaches, which often rely on
employing separate encoders like prosody and content encoders to handle
different aspects of the source speech, our model handles various information
of speech in an end-to-end manner. Experiments have demonstrated the impressive
capabilities of our model in comprehending instructions and delivering
reasonable results.</description><identifier>DOI: 10.48550/arxiv.2309.14324</identifier><language>eng</language><subject>Computer Science - Computation and Language ; Computer Science - Learning ; Computer Science - Sound</subject><creationdate>2023-09</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,881</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2309.14324$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2309.14324$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Kuan, Chun-Yi</creatorcontrib><creatorcontrib>Li, Chen An</creatorcontrib><creatorcontrib>Hsu, Tsu-Yuan</creatorcontrib><creatorcontrib>Lin, Tse-Yang</creatorcontrib><creatorcontrib>Chung, Ho-Lam</creatorcontrib><creatorcontrib>Chang, Kai-Wei</creatorcontrib><creatorcontrib>Chang, Shuo-yiin</creatorcontrib><creatorcontrib>Lee, Hung-yi</creatorcontrib><title>Towards General-Purpose Text-Instruction-Guided Voice Conversion</title><description>This paper introduces a novel voice conversion (VC) model, guided by text
instructions such as "articulate slowly with a deep tone" or "speak in a
cheerful boyish voice". Unlike traditional methods that rely on reference
utterances to determine the attributes of the converted speech, our model adds
versatility and specificity to voice conversion. The proposed VC model is a
neural codec language model which processes a sequence of discrete codes,
resulting in the code sequence of converted speech. It utilizes text
instructions as style prompts to modify the prosody and emotional information
of the given speech. In contrast to previous approaches, which often rely on
employing separate encoders like prosody and content encoders to handle
different aspects of the source speech, our model handles various information
of speech in an end-to-end manner. Experiments have demonstrated the impressive
capabilities of our model in comprehending instructions and delivering
reasonable results.</description><subject>Computer Science - Computation and Language</subject><subject>Computer Science - Learning</subject><subject>Computer Science - Sound</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotj7FOwzAURb0woMIHMJEfcHhubMfZQBGESpVgiFgjx35PslTiyk5K-XtCYbpXdzhXh7E7AaU0SsGDTedwKrcVNKWQ1VZes8c-ftnkc9HhhMke-PuSjjFj0eN55rspz2lxc4gT75bg0RcfMTgs2jidMOV1v2FXZA8Zb_9zw_qX57595fu3btc-7bnVteRWIDgU5CQppUEbv_6PayfXoFLGNBrECKMhoywBibr2DoB0TQ3oUVUbdv-HvSgMxxQ-bfoeflWGi0r1A1N6RBo</recordid><startdate>20230925</startdate><enddate>20230925</enddate><creator>Kuan, Chun-Yi</creator><creator>Li, Chen An</creator><creator>Hsu, Tsu-Yuan</creator><creator>Lin, Tse-Yang</creator><creator>Chung, Ho-Lam</creator><creator>Chang, Kai-Wei</creator><creator>Chang, Shuo-yiin</creator><creator>Lee, Hung-yi</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20230925</creationdate><title>Towards General-Purpose Text-Instruction-Guided Voice Conversion</title><author>Kuan, Chun-Yi ; Li, Chen An ; Hsu, Tsu-Yuan ; Lin, Tse-Yang ; Chung, Ho-Lam ; Chang, Kai-Wei ; Chang, Shuo-yiin ; Lee, Hung-yi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a674-a1e0ce1fc4f556068d143bf55fc9e55889601b0b8f85af0f177dc00f67f906b53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Computer Science - Computation and Language</topic><topic>Computer Science - Learning</topic><topic>Computer Science - Sound</topic><toplevel>online_resources</toplevel><creatorcontrib>Kuan, Chun-Yi</creatorcontrib><creatorcontrib>Li, Chen An</creatorcontrib><creatorcontrib>Hsu, Tsu-Yuan</creatorcontrib><creatorcontrib>Lin, Tse-Yang</creatorcontrib><creatorcontrib>Chung, Ho-Lam</creatorcontrib><creatorcontrib>Chang, Kai-Wei</creatorcontrib><creatorcontrib>Chang, Shuo-yiin</creatorcontrib><creatorcontrib>Lee, Hung-yi</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Kuan, Chun-Yi</au><au>Li, Chen An</au><au>Hsu, Tsu-Yuan</au><au>Lin, Tse-Yang</au><au>Chung, Ho-Lam</au><au>Chang, Kai-Wei</au><au>Chang, Shuo-yiin</au><au>Lee, Hung-yi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Towards General-Purpose Text-Instruction-Guided Voice Conversion</atitle><date>2023-09-25</date><risdate>2023</risdate><abstract>This paper introduces a novel voice conversion (VC) model, guided by text
instructions such as "articulate slowly with a deep tone" or "speak in a
cheerful boyish voice". Unlike traditional methods that rely on reference
utterances to determine the attributes of the converted speech, our model adds
versatility and specificity to voice conversion. The proposed VC model is a
neural codec language model which processes a sequence of discrete codes,
resulting in the code sequence of converted speech. It utilizes text
instructions as style prompts to modify the prosody and emotional information
of the given speech. In contrast to previous approaches, which often rely on
employing separate encoders like prosody and content encoders to handle
different aspects of the source speech, our model handles various information
of speech in an end-to-end manner. Experiments have demonstrated the impressive
capabilities of our model in comprehending instructions and delivering
reasonable results.</abstract><doi>10.48550/arxiv.2309.14324</doi><oa>free_for_read</oa></addata></record> |
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subjects | Computer Science - Computation and Language Computer Science - Learning Computer Science - Sound |
title | Towards General-Purpose Text-Instruction-Guided Voice Conversion |
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