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

arXiv: https://arxiv.org/abs/2202.10020 · HTML: https://mlsanity.com/paper/2202.10020/

## Key facts

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

## Built on by (most cited first, 24 of 24)

- [An Efficient Temporary Deepfake Location Approach Based Embeddings for Partially Spoofed Audio Detection](https://mlsanity.com/paper/2309.03036/) (2023)
- [CLAD: Robust Audio Deepfake Detection Against Manipulation Attacks with Contrastive Learning](https://mlsanity.com/paper/2404.15854/) (2024)
- [Pre-Avatar: An Automatic Presentation Generation Framework Leveraging Talking Avatar](https://mlsanity.com/paper/2210.06877/) (2022)
- [Hierarchical Topology Isomorphism Expertise Embedded Graph Contrastive Learning](https://mlsanity.com/paper/2312.14222/) (2023)
- [Prosody-Adaptable Audio Codecs for Zero-Shot Voice Conversion via In-Context Learning](https://mlsanity.com/paper/2505.15402/) (2025)
- [Generalized Source Tracing: Detecting Novel Audio Deepfake Algorithm with Real Emphasis and Fake Dispersion Strategy](https://mlsanity.com/paper/2406.03240/) (2024)
- [Catch You and I Can: Revealing Source Voiceprint Against Voice Conversion](https://mlsanity.com/paper/2302.12434/) (2023)
- [Reimagining Speech: A Scoping Review of Deep Learning-Powered Voice Conversion](https://mlsanity.com/paper/2311.08104/) (2023)
- [CoMelSinger: Discrete Token-Based Zero-Shot Singing Synthesis With Structured Melody Control and Guidance](https://mlsanity.com/paper/2509.19883/) (2025)
- [Semi-Supervised Learning Based on Reference Model for Low-resource TTS](https://mlsanity.com/paper/2210.14723/) (2022)
- [Improved disentangled speech representations using contrastive learning in factorized hierarchical variational autoencoder](https://mlsanity.com/paper/2211.08191/) (2022)
- [Generative Adversarial Network based Voice Conversion: Techniques, Challenges, and Recent Advancements](https://mlsanity.com/paper/2504.19197/) (2025)
- [Fast-VGAN: Lightweight Voice Conversion with Explicit Control of F0 and Duration Parameters](https://mlsanity.com/paper/2507.04817/) (2025)
- [Graph is a Natural Regularization: Revisiting Vector Quantization for Graph Representation Learning](https://mlsanity.com/paper/2508.06588/) (2025)
- [Boosting Star-GANs for Voice Conversion with Contrastive Discriminator](https://mlsanity.com/paper/2209.10088/) (2022)
- [Improving Speech Representation Learning via Speech-level and Phoneme-level Masking Approach](https://mlsanity.com/paper/2210.13805/) (2022)
- [Takin-VC: Expressive Zero-Shot Voice Conversion via Adaptive Hybrid Content Encoding and Enhanced Timbre Modeling](https://mlsanity.com/paper/2410.01350/) (2024)
- [NE-PADD: Leveraging Named Entity Knowledge for Robust Partial Audio Deepfake Detection via Attention Aggregation](https://mlsanity.com/paper/2509.03829/) (2025)
- [Adapitch: Adaption Multi-Speaker Text-to-Speech Conditioned on Pitch Disentangling with Untranscribed Data](https://mlsanity.com/paper/2210.13803/) (2022)
- [MetaSpeech: Speech Effects Switch Along with Environment for Metaverse](https://mlsanity.com/paper/2210.13811/) (2022)
- [Improving Imbalanced Text Classification with Dynamic Curriculum Learning](https://mlsanity.com/paper/2210.14724/) (2022)
- [DDSP-QbE++: Improving Speech Quality for Speech Anonymisation for Atypical Speech](https://mlsanity.com/paper/2604.09246/) (2026)
- [V.O.I.C.E (Voice, Ownership, Identity, Control, Expression): Risk Taxonomy of Synthetic Voice Generation From Empirical Data](https://mlsanity.com/paper/2604.24794/) (2026)
- [A survey of AI-generated voices and their detection](https://mlsanity.com/paper/2608.15411/) (2026)

## 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.