S3PRL-VC: Open-Source Voice Conversion Framework with Self-Supervised Speech Representations
S3PRL-VC: Open-Source Voice Conversion Framework with Self-Supervised Speech Representations
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DOI：
10.1109/icassp43922.2022.9746430
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发表时间：
2021-10
期刊：
影响因子：
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通讯作者：
Wen-Chin Huang;Shu-Wen Yang;Tomoki Hayashi;Hung-yi Lee;Shinji Watanabe;T. Toda
中科院分区：
文献类型：
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作者：
Wen-Chin Huang;Shu-Wen Yang;Tomoki Hayashi;Hung-yi Lee;Shinji Watanabe;T. Toda
介绍了一种基于S3PRL工具包的开源语音转换框架S3PRL-VC。在识别合成VC的背景下，自监督语音表示(S3R)具有取代现有VC系统所采用的昂贵的监督表示的潜力。此外，我们认为VC是一个很好的S3R分析的探索性任务。在这项工作中，我们通过基准测试对VCC2020中的两个任务，即语言内/跨语言任意对一(A2O)VC以及任意对任意(A2A)设置进行了一系列深入的分析。我们还比较了不同的S3R和VCC2020中使用监督表示的TOP系统。系统的客观和主观评价表明，S3R在A2O环境下与VCC2020 TOP系统在相似性方面具有可比性，在基于S3R的A2AVC方面达到了最先进的水平。我们相信，广泛的分析以及工具包本身不仅有助于S3R社区，也有助于VC社区。代码库现在是开源的1。
This paper introduces S3PRL-VC, an open-source voice conversion (VC) framework based on the S3PRL toolkit. In the context of recognition-synthesis VC, self-supervised speech representation (S3R) is valuable in its potential to replace the expensive supervised representation adopted by state-of-the-art VC systems. Moreover, we claim that VC is a good probing task for S3R analysis. In this work, we provide a series of in-depth analyses by benchmarking on the two tasks in VCC2020, namely intra-/cross-lingual any-to-one (A2O) VC, as well as an any-to-any (A2A) setting. We also provide comparisons between not only different S3Rs but also top systems in VCC2020 with supervised representations. Systematic objective and subjective evaluation were conducted, and we show that S3R is comparable with VCC2020 top systems in the A2O setting in terms of similarity, and achieves state-of-the-art in S3R-based A2A VC. We believe the extensive analysis, as well as the toolkit itself, contribute to not only the S3R community but also the VC community. The codebase is now open-sourced1.