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Invertible Voice Conversion

arXiv 2201.10687 ↗First posted 26 Jan 2022eess.AS cs.LG cs.SDcited by 0

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Abstract (arXiv)

In this paper, we propose an invertible deep learning framework called INVVC for voice conversion. It is designed against the possible threats that inherently come along with voice conversion systems. Specifically, we develop an invertible framework that makes the source identity traceable. The framework is built on a series of invertible 1×1 convolutions and flows consisting of affine coupling layers. We apply the proposed framework to one-to-one voice conversion and many-to-one conversion using parallel training data. Experimental results show that this approach yields impressive performance on voice conversion and, moreover, the converted results can be reversed back to the source inputs utilizing the same parameters as in forwarding.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 32 references that are in the map. First posted on arXiv 26 Jan 2022.

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18 of 32 references.

References · 18

  1. 2017Attention Is All You Needcited by 60,361
  2. 2016Density estimation using Real NVPcited by 2,042
  3. 2016WaveNet: A Generative Model for Raw Audiocited by 1,722
  4. 2018Glow: Generative Flow with Invertible 1x1 Convolutionscited by 1,545
  5. 2014NICE: Non-linear Independent Components Estimationcited by 1,058
  6. 2020ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verificationcited by 628
  7. 2018WaveGlow: A Flow-based Generative Network for Speech Synthesiscited by 309
  8. 2018Efficient Neural Audio Synthesiscited by 219
  9. 2019AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Losscited by 192
  10. 2020End-to-end anti-spoofing with RawNet2cited by 164
  11. 2020An Overview of Voice Conversion and its Challenges: From Statistical Modeling to Deep Learningcited by 112
  12. 2019One-shot Voice Conversion by Separating Speaker and Content Representations with Instance Normalizationcited by 82
6 more
  1. 2020FragmentVC: Any-to-Any Voice Conversion by End-to-End Extracting and Fusing Fine-Grained Voice Fragments With Attentioncited by 37
  2. 2019Blow: a single-scale hyperconditioned flow for non-parallel raw-audio voice conversioncited by 29
  3. 2018High-quality nonparallel voice conversion based on cycle-consistent adversarial networkcited by 13
  4. 2020GAZEV: GAN-Based Zero-Shot Voice Conversion over Non-parallel Speech Corpuscited by 10
  5. 2020Efficient neural speech synthesis for low-resource languages through multilingual modelingcited by 8
  6. 2021Building Bilingual and Code-Switched Voice Conversion with Limited Training Data Using Embedding Consistency Losscited by 2

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