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Electrical Engineering and Systems Science > Audio and Speech Processing

arXiv:2110.09928 (eess)
[Submitted on 18 Oct 2021 (v1), last revised 20 Oct 2021 (this version, v2)]

Title:CycleFlow: Purify Information Factors by Cycle Loss

Authors:Haoran Sun, Chen Chen, Lantian Li, Dong Wang
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Abstract:SpeechFlow is a powerful factorization model based on information bottleneck (IB), and its effectiveness has been reported by several studies. A potential problem of SpeechFlow, however, is that if the IB channels are not well designed, the resultant factors cannot be well disentangled. In this study, we propose a CycleFlow model that combines random factor substitution and cycle loss to solve this problem. Experiments on voice conversion tasks demonstrate that this simple technique can effectively reduce mutual information among individual factors, and produce clearly better conversion than the IB-based SpeechFlow. CycleFlow can also be used as a powerful tool for speech editing. We demonstrate this usage by an emotion perception experiment.
Comments: Submitted to ICASSP 2022
Subjects: Audio and Speech Processing (eess.AS); Machine Learning (cs.LG); Sound (cs.SD)
Cite as: arXiv:2110.09928 [eess.AS]
  (or arXiv:2110.09928v2 [eess.AS] for this version)
  https://doi.org/10.48550/arXiv.2110.09928

Submission history

From: Lantian Li Mr. [view email]
[v1] Mon, 18 Oct 2021 13:19:08 UTC (704 KB)
[v2] Wed, 20 Oct 2021 03:06:26 UTC (768 KB)
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