mlsanityData to 14 Oct 2026

AVQVC: One-shot Voice Conversion by Vector Quantization with applying contrastive learning

arXiv 2202.10020 ↗First posted 21 Feb 2022cs.SD eess.AScited by 24

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Abstract (arXiv)

Voice Conversion(VC) refers to changing the timbre of a speech while retaining the discourse content. Recently, many works have focused on disentangle-based learning techniques to separate the timbre and the linguistic content information from a speech signal. Once successful, voice conversion will be feasible and straightforward. This paper proposed a novel one-shot voice conversion framework based on vector quantization voice conversion (VQVC) and AutoVC, called AVQVC. A new training method is applied to VQVC to separate content and timbre information from speech more effectively. The result shows that this approach has better performance than VQVC in separating content and timbre to improve the sound quality of generated speech.

Key facts. Cited by 24 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 Feb 2022.

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Citing papers per year: 2022: 8; 2023: 4; 2024: 3; 2025: 6; 2026: 3.

24 papers.

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  1. 2023An Efficient Temporary Deepfake Location Approach Based Embeddings for Partially Spoofed Audio Detectioncited by 10
  2. 2024CLAD: Robust Audio Deepfake Detection Against Manipulation Attacks with Contrastive Learningcited by 9
  3. 2022Pre-Avatar: An Automatic Presentation Generation Framework Leveraging Talking Avatarcited by 6
  4. 2023Hierarchical Topology Isomorphism Expertise Embedded Graph Contrastive Learningcited by 6
  5. 2025Prosody-Adaptable Audio Codecs for Zero-Shot Voice Conversion via In-Context Learningcited by 6
  6. 2024Generalized Source Tracing: Detecting Novel Audio Deepfake Algorithm with Real Emphasis and Fake Dispersion Strategycited by 5
  7. 2023Catch You and I Can: Revealing Source Voiceprint Against Voice Conversioncited by 4
  8. 2023Reimagining Speech: A Scoping Review of Deep Learning-Powered Voice Conversioncited by 3
  9. 2025CoMelSinger: Discrete Token-Based Zero-Shot Singing Synthesis With Structured Melody Control and Guidancecited by 3
  10. 2022Semi-Supervised Learning Based on Reference Model for Low-resource TTScited by 2
  11. 2022Improved disentangled speech representations using contrastive learning in factorized hierarchical variational autoencodercited by 2
  12. 2025Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancementscited by 2
12 more
  1. 2025Fast-VGAN: Lightweight Voice Conversion with Explicit Control of F0 and Duration Parameterscited by 2
  2. 2025Graph is a Natural Regularization: Revisiting Vector Quantization for Graph Representation Learningcited by 2
  3. 2022Boosting Star-GANs for Voice Conversion with Contrastive Discriminatorcited by 1
  4. 2022Improving Speech Representation Learning via Speech-level and Phoneme-level Masking Approachcited by 1
  5. 2024Takin-VC: Expressive Zero-Shot Voice Conversion via Adaptive Hybrid Content Encoding and Enhanced Timbre Modelingcited by 1
  6. 2025NE-PADD: Leveraging Named Entity Knowledge for Robust Partial Audio Deepfake Detection via Attention Aggregationcited by 1
  7. 2022Adapitch: Adaption Multi-Speaker Text-to-Speech Conditioned on Pitch Disentangling with Untranscribed Datacited by 0
  8. 2022MetaSpeech: Speech Effects Switch Along with Environment for Metaversecited by 0
  9. 2022Improving Imbalanced Text Classification with Dynamic Curriculum Learningcited by 0
  10. 2026DDSP-QbE++: Improving Speech Quality for Speech Anonymisation for Atypical Speechcited by 0
  11. 2026V.O.I.C.E (Voice, Ownership, Identity, Control, Expression): Risk Taxonomy of Synthetic Voice Generation From Empirical Datacited by 0
  12. 2026A survey of AI-generated voices and their detectioncited by 0