Takenori Yoshimura, Takato Fujimoto, Keiichiro Oura, K. Tokuda
{"title":"SPTK4: An Open-Source Software Toolkit for Speech Signal Processing","authors":"Takenori Yoshimura, Takato Fujimoto, Keiichiro Oura, K. Tokuda","doi":"10.21437/ssw.2023-33","DOIUrl":null,"url":null,"abstract":"The Speech Signal Processing ToolKit (SPTK) is an open-source suite of speech signal processing tools, which has been developed and maintained by the SPTK working group and has widely contributed to the speech signal processing community since 1998. Although SPTK has reached over a hundred thousand downloads, the concepts as well as the features have not yet been widely disseminated. This paper gives an overview of SPTK and demonstrations to provide a better understanding of the toolkit. We have recently developed its differentiable Py-Torch version, diffsptk , to adapt to advancements in the deep learning field. The details of diffsptk are also presented in this paper. We hope that the toolkit will help developers and researchers working in the field of speech signal processing.","PeriodicalId":346639,"journal":{"name":"12th ISCA Speech Synthesis Workshop (SSW2023)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"12th ISCA Speech Synthesis Workshop (SSW2023)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21437/ssw.2023-33","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The Speech Signal Processing ToolKit (SPTK) is an open-source suite of speech signal processing tools, which has been developed and maintained by the SPTK working group and has widely contributed to the speech signal processing community since 1998. Although SPTK has reached over a hundred thousand downloads, the concepts as well as the features have not yet been widely disseminated. This paper gives an overview of SPTK and demonstrations to provide a better understanding of the toolkit. We have recently developed its differentiable Py-Torch version, diffsptk , to adapt to advancements in the deep learning field. The details of diffsptk are also presented in this paper. We hope that the toolkit will help developers and researchers working in the field of speech signal processing.