TTS持续时间模型对新说话者的有效自适应

Chilin Shih, Wentao Gu, J. V. Santen
{"title":"TTS持续时间模型对新说话者的有效自适应","authors":"Chilin Shih, Wentao Gu, J. V. Santen","doi":"10.21437/ICSLP.1998-5","DOIUrl":null,"url":null,"abstract":"This paper discusses a methodology using a minimal set of sentences to adapt an existing TTS duration model to capture interspeaker variations. The assumption is that the original duration database contains information of both language-specific and speaker-specific duration characteristics. In training a duration model for a new speaker, only the speaker-specific information needs to be modeled, therefore the size of the training data can be reduced drastically. Results from several experiments are compared and discussed.","PeriodicalId":90685,"journal":{"name":"Proceedings : ICSLP. International Conference on Spoken Language Processing","volume":"151 1","pages":"105-110"},"PeriodicalIF":0.0000,"publicationDate":"1998-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"Efficient adaptation of TTS duration model to new speakers\",\"authors\":\"Chilin Shih, Wentao Gu, J. V. Santen\",\"doi\":\"10.21437/ICSLP.1998-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper discusses a methodology using a minimal set of sentences to adapt an existing TTS duration model to capture interspeaker variations. The assumption is that the original duration database contains information of both language-specific and speaker-specific duration characteristics. In training a duration model for a new speaker, only the speaker-specific information needs to be modeled, therefore the size of the training data can be reduced drastically. Results from several experiments are compared and discussed.\",\"PeriodicalId\":90685,\"journal\":{\"name\":\"Proceedings : ICSLP. International Conference on Spoken Language Processing\",\"volume\":\"151 1\",\"pages\":\"105-110\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1998-11-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings : ICSLP. International Conference on Spoken Language Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.21437/ICSLP.1998-5\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings : ICSLP. International Conference on Spoken Language Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21437/ICSLP.1998-5","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 11

摘要

本文讨论了一种方法,使用最小的句子集来适应现有的TTS持续时间模型,以捕捉说话者之间的变化。假设原始持续时间数据库包含特定于语言和特定于讲话者的持续时间特征的信息。在训练一个新的说话人的持续时间模型时,只需要对说话人的特定信息进行建模,因此可以大大减少训练数据的大小。对几个实验的结果进行了比较和讨论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Efficient adaptation of TTS duration model to new speakers
This paper discusses a methodology using a minimal set of sentences to adapt an existing TTS duration model to capture interspeaker variations. The assumption is that the original duration database contains information of both language-specific and speaker-specific duration characteristics. In training a duration model for a new speaker, only the speaker-specific information needs to be modeled, therefore the size of the training data can be reduced drastically. Results from several experiments are compared and discussed.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信