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DisC-VC: Disentangled and F0-Controllable Neural Voice Conversion

arXiv 2210.11059 ↗First posted 20 Oct 2022eess.AS cs.SD stat.MLcited by 0

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Abstract (arXiv)

Voice conversion is a task to convert a non-linguistic feature of a given utterance. Since naturalness of speech strongly depends on its pitch pattern, in some applications, it would be desirable to keep the original rise/fall pitch pattern while changing the speaker identity. Some of the existing methods address this problem by either using a source-filter model or developing a neural network that takes an F0 pattern as input to the model. Although the latter approach can achieve relatively high sound quality compared to the former one, there is no consideration for discrepancy between the target and generated F0 patterns in its training process. In this paper, we propose a new variational-autoencoder-based voice conversion model accompanied by an auxiliary network, which ensures that the conversion result correctly reflects the specified F0/timbre information. We show the effectiveness of the proposed method by objective and subjective evaluations.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 23 of its 42 references that are in the map. First posted on arXiv 20 Oct 2022.

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23 of 42 references.

References · 23

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11 more
  1. 2017Voice Conversion from Unaligned Corpora using Variational Autoencoding Wasserstein Generative Adversarial Networkscited by 38
  2. 2019StarGAN-VC2: Rethinking Conditional Methods for StarGAN-Based Voice Conversioncited by 37
  3. 2020FragmentVC: Any-to-Any Voice Conversion by End-to-End Extracting and Fusing Fine-Grained Voice Fragments With Attentioncited by 37
  4. 2020F0-consistent many-to-many non-parallel voice conversion via conditional autoencodercited by 35
  5. 2019Blow: a single-scale hyperconditioned flow for non-parallel raw-audio voice conversioncited by 29
  6. 2019Parrotron: An End-to-End Speech-to-Speech Conversion Model and its Applications to Hearing-Impaired Speech and Speech Separationcited by 22
  7. 2020Any-to-One Sequence-to-Sequence Voice Conversion using Self-Supervised Discrete Speech Representationscited by 18
  8. 2020VoiceGrad: Non-Parallel Any-to-Many Voice Conversion with Annealed Langevin Dynamicscited by 14
  9. 2021Assem-VC: Realistic Voice Conversion by Assembling Modern Speech Synthesis Techniquescited by 11
  10. 2022Text-free non-parallel many-to-many voice conversion using normalising flowscited by 10
  11. 2020Transferring Source Style in Non-Parallel Voice Conversioncited by 7

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