One-Shot Speaker Adaptation Based on Initialization by Generative Adversarial Networks for TTS
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Jaeuk | - |
dc.contributor.author | Chang, Joon-Hyuk | - |
dc.date.accessioned | 2022-12-20T06:25:00Z | - |
dc.date.available | 2022-12-20T06:25:00Z | - |
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/173087 | - |
dc.description.abstract | Speaker adaptation for personalizing text-to-speech (TTS) has become increasingly important. Herein, we propose a novel adaptation using a few seconds of data obtained from an unseen speaker. We first use a speaker embedding lookup table to train a multi-speaker TTS model, wherein each speaker embedding in the lookup table contains information representing a speaker's timbre. We propose an initial embedding predictor that extracts initial embedding suitable for the adaptation of unseen speakers. We use trained speaker embeddings to train the initial embedding predictor. Further, adversarial training is applied to improve the performance. After adversarial training, the initial embedding predictor infers the unseen speaker's initial embedding, and it is fine-tuned. As the initial embedding contains timbre information of the unseen speaker, adaptation is achieved faster and with less data than with conventional methods. We validate the performance with a mean opinion score (MOS) and demonstrate that adaptation is feasible with only 5 s of data. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | International Speech Communication Association | - |
dc.title | One-Shot Speaker Adaptation Based on Initialization by Generative Adversarial Networks for TTS | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Chang, Joon-Hyuk | - |
dc.identifier.doi | 10.21437/Interspeech.2022-934 | - |
dc.identifier.scopusid | 2-s2.0-85140087242 | - |
dc.identifier.wosid | 000900724503030 | - |
dc.identifier.bibliographicCitation | Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, v.2022-September, pp.2978 - 2982 | - |
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 | 2978 | - |
dc.citation.endPage | 2982 | - |
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 | Generative adversarial networks | - |
dc.subject.keywordPlus | Speech communication | - |
dc.subject.keywordPlus | Table lookup | - |
dc.subject.keywordPlus | Embeddings | - |
dc.subject.keywordPlus | Conventional methods | - |
dc.subject.keywordPlus | Embeddings | - |
dc.subject.keywordPlus | Mean opinion scores | - |
dc.subject.keywordPlus | Multi-speaker | - |
dc.subject.keywordPlus | Performance | - |
dc.subject.keywordPlus | Speaker adaptation | - |
dc.subject.keywordPlus | Speech models | - |
dc.subject.keywordPlus | Text to speech | - |
dc.subject.keywordPlus | Voice cloning | - |
dc.subject.keywordAuthor | multi-speaker | - |
dc.subject.keywordAuthor | speaker adaptation | - |
dc.subject.keywordAuthor | voice cloning | - |
dc.identifier.url | https://www.isca-speech.org/archive/interspeech_2022/lee22h_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.