Adversarial and Sequential Training for Cross-lingual Prosody Transfer TTS
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Min-Kyung | - |
dc.contributor.author | Chang, Joon-Hyuk | - |
dc.date.accessioned | 2022-12-20T06:25:03Z | - |
dc.date.available | 2022-12-20T06:25:03Z | - |
dc.date.created | 2022-11-02 | - |
dc.date.issued | 2022-09 | - |
dc.identifier.issn | 2308-457X | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/173088 | - |
dc.description.abstract | This study presents a method for improving the performance of the text-to-speech (TTS) model by using three global speech-style representations: language, speaker, and prosody. Synthesizing different languages and prosody in the speaker's voice regardless of their own language and prosody is possible. To construct the embedding of each representation conditioned in the TTS model such that it is independent of the other representations, we propose an adversarial training method for the general architecture of TTS models. Furthermore, we introduce a sequential training method that includes rehearsal-based continual learning to train complex and small amounts of data without forgetting previously learned information. The experimental results show that the proposed method can generate good-quality speech and yield high similarity for speakers and prosody, even for representations that the speaker in the dataset does not contain. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | International Speech Communication Association | - |
dc.title | Adversarial and Sequential Training for Cross-lingual Prosody Transfer TTS | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Chang, Joon-Hyuk | - |
dc.identifier.doi | 10.21437/Interspeech.2022-865 | - |
dc.identifier.scopusid | 2-s2.0-85140092669 | - |
dc.identifier.wosid | 000900724504148 | - |
dc.identifier.bibliographicCitation | Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, v.2022-September, pp.4556 - 4560 | - |
dc.relation.isPartOf | Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH | - |
dc.citation.title | Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH | - |
dc.citation.volume | 2022-September | - |
dc.citation.startPage | 4556 | - |
dc.citation.endPage | 4560 | - |
dc.type.rims | ART | - |
dc.type.docType | Proceedings Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Acoustics | - |
dc.relation.journalResearchArea | Audiology & Speech-Language Pathology | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Acoustics | - |
dc.relation.journalWebOfScienceCategory | Audiology & Speech-Language Pathology | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.subject.keywordPlus | Adversarial training | - |
dc.subject.keywordPlus | Continual learning | - |
dc.subject.keywordPlus | Cross-lingual | - |
dc.subject.keywordPlus | Performance | - |
dc.subject.keywordPlus | Prosody | - |
dc.subject.keywordPlus | Representation languages | - |
dc.subject.keywordPlus | Speech models | - |
dc.subject.keywordPlus | Speech style | - |
dc.subject.keywordPlus | Text to speech | - |
dc.subject.keywordPlus | Training methods | - |
dc.subject.keywordPlus | Speech communication | - |
dc.subject.keywordAuthor | adversarial training | - |
dc.subject.keywordAuthor | continual learning | - |
dc.subject.keywordAuthor | cross-lingual | - |
dc.subject.keywordAuthor | prosody | - |
dc.subject.keywordAuthor | text-to-speech | - |
dc.identifier.url | https://www.isca-speech.org/archive/interspeech_2022/kim22g_interspeech.html | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea+82-2-2220-1365
COPYRIGHT © 2021 HANYANG UNIVERSITY.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.