Unsupervised Speech Decomposition via Triple Information Bottleneck

Part of Proceedings of the International Conference on Machine Learning 1 pre-proceedings (ICML 2020)

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Authors

Kaizhi Qian, Yang Zhang, Shiyu Chang, Mark Hasegawa-Johnson, David Cox

Abstract

<p>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 SpeechFlow, which can blindly decompose speech into its four components by introducing three carefully designed information bottlenecks. SpeechFlow is among the first algorithms that can separately perform style transfer on timbre, pitch and rhythm without text labels.</p>