mlsanityData to 7 Oct 2026

A unified one-shot prosody and speaker conversion system with self-supervised discrete speech units

arXiv 2211.06535 ↗First posted 12 Nov 2022eess.AS cs.CL cs.LGcited by 3

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Abstract (arXiv)

We present a unified system to realize one-shot voice conversion (VC) on the pitch, rhythm, and speaker attributes. Existing works generally ignore the correlation between prosody and language content, leading to the degradation of naturalness in converted speech. Additionally, the lack of proper language features prevents these systems from accurately preserving language content after conversion. To address these issues, we devise a cascaded modular system leveraging self-supervised discrete speech units as language representation. These discrete units provide duration information essential for rhythm modeling. Our system first extracts utterance-level prosody and speaker representations from the raw waveform. Given the prosody representation, a prosody predictor estimates pitch, energy, and duration for each discrete unit in the utterance. A synthesizer further reconstructs speech based on the predicted prosody, speaker representation, and discrete units. Experiments show that our system outperforms previous approaches in naturalness, intelligibility, speaker transferability, and prosody transferability. Code and samples are publicly available.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 24 of its 30 references that are in the map. First posted on arXiv 12 Nov 2022.

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24 of 30 references.

References · 24

  1. 2016Layer Normalizationcited by 5,579
  2. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,853
  3. 2021HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Unitscited by 2,301
  4. 2020HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesiscited by 1,139
  5. 2016Least Squares Generative Adversarial Networkscited by 1,000
  6. 2019LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speechcited by 639
  7. 2019AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Losscited by 189
  8. 2022Dawn of the transformer era in speech emotion recognition: closing the valence gapcited by 155
  9. 2020Seen and Unseen emotional style transfer for voice conversion with a new emotional speech datasetcited by 119
  10. 2021Text-Free Prosody-Aware Generative Spoken Language Modelingcited by 119
  11. 2018CREPE: A Convolutional Representation for Pitch Estimationcited by 109
  12. 2020Unsupervised Speech Decomposition via Triple Information Bottleneckcited by 87
12 more
  1. 2021Neural Analysis and Synthesis: Reconstructing Speech from Self-Supervised Representationscited by 81
  2. 2021VQMIVC: Vector Quantization and Mutual Information-Based Unsupervised Speech Representation Disentanglement for One-shot Voice Conversioncited by 63
  3. 2019ESPnet-TTS: Unified, Reproducible, and Integratable Open Source End-to-End Text-to-Speech Toolkitcited by 60
  4. 2020F0-consistent many-to-many non-parallel voice conversion via conditional autoencodercited by 35
  5. 2020VQVC+: One-Shot Voice Conversion by Vector Quantization and U-Net architecturecited by 34
  6. 2022textless-lib: a Library for Textless Spoken Language Processingcited by 28
  7. 2022SpeechSplit 2.0: Unsupervised speech disentanglement for voice conversion Without tuning autoencoder Bottleneckscited by 27
  8. 2021Visualizing Data using GTSNEcited by 23
  9. 2021S3PRL-VC: Open-source Voice Conversion Framework with Self-supervised Speech Representationscited by 22
  10. 2021Fine-grained style control in Transformer-based Text-to-speech Synthesiscited by 15
  11. 2022Speech Representation Disentanglement with Adversarial Mutual Information Learning for One-shot Voice Conversioncited by 14
  12. 2019Unsupervised End-to-End Learning of Discrete Linguistic Units for Voice Conversioncited by 12

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Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 1.

3 papers.