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Unsupervised Speech Decomposition via Triple Information Bottleneck
论文
论文
发布时间2020-04-23
发表ICML 2020 1 · arXiv:2004.11284
作者:Yang Zhang,Mark Hasegawa-Johnson,Kaizhi Qian,Shiyu Chang,David Cox
详细介绍
Speech information can be roughly decomposed into four components: language content, timbre, pitch, and rhythm. Obtaining disentangled representations of these components is useful in many speech analysis and generation applications. Recently, state-of-the-art voice conversion systems have led to speech representations that can disentangle speaker-dependent and independent information. However, these systems can only disentangle timbre, while information about pitch, rhythm and content is still mixed together. Further disentangling the remaining speech components is an under-determined problem in the absence of explicit annotations for each component, which are difficult and expensive to obtain. In this paper, we propose SpeechSplit, which can blindly decompose speech into its four components by introducing three carefully designed information bottlenecks. SpeechSplit is among the first algorithms that can separately perform style transfer on timbre, pitch and rhythm without text labels. Our code is publicly available at https://github.com/auspicious3000/SpeechSplit.
代码仓库 (6)
auspicious3000/SpeechSplit官方PyTorch
tobwei/disentintelPyTorch
FurkanGozukara/SpeechSplitTestPyTorch
auspicious3000/autovcPyTorch
CODEJIN/SPEECHSPLITPyTorch
keonlee9420/STYLERPyTorch
