# Voice-preserving Zero-shot Multiple Accent Conversion

arXiv: https://arxiv.org/abs/2211.13282 · HTML: https://mlsanity.com/paper/2211.13282/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 23 Nov 2022.

## Builds on (most cited first, 7 of 7)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://mlsanity.com/paper/2010.05646/) (2020)
- [LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech](https://mlsanity.com/paper/1904.02882/) (2019)
- [Speech Resynthesis from Discrete Disentangled Self-Supervised Representations](https://mlsanity.com/paper/2104.00355/) (2021)
- [Learning to Speak Fluently in a Foreign Language: Multilingual Speech Synthesis and Cross-Language Voice Cloning](https://mlsanity.com/paper/1907.04448/) (2019)
- [Accent and Speaker Disentanglement in Many-to-many Voice Conversion](https://mlsanity.com/paper/2011.08609/) (2020)
- [Unsupervised Learning of Disentangled Speech Content and Style Representation](https://mlsanity.com/paper/2010.12973/) (2020)

## Built on by (most cited first, 17 of 17)

- [The Ethical Implications of Generative Audio Models: A Systematic Literature Review](https://mlsanity.com/paper/2307.05527/) (2023)
- [Forensic deepfake audio detection using segmental speech features](https://mlsanity.com/paper/2505.13847/) (2025)
- [VoiceShop: A Unified Speech-to-Speech Framework for Identity-Preserving Zero-Shot Voice Editing](https://mlsanity.com/paper/2404.06674/) (2024)
- [Transfer the linguistic representations from TTS to accent conversion with non-parallel data](https://mlsanity.com/paper/2401.03538/) (2024)
- [Accent conversion using discrete units with parallel data synthesized from controllable accented TTS](https://mlsanity.com/paper/2410.03734/) (2024)
- [Improving Pronunciation and Accent Conversion through Knowledge Distillation And Synthetic Ground-Truth from Native TTS](https://mlsanity.com/paper/2410.14997/) (2024)
- [Pairwise Evaluation of Accent Similarity in Speech Synthesis](https://mlsanity.com/paper/2505.14410/) (2025)
- [Non-autoregressive real-time Accent Conversion model with voice cloning](https://mlsanity.com/paper/2405.13162/) (2024)
- [Unifying Speech Recognition, Synthesis and Conversion with Autoregressive Transformers](https://mlsanity.com/paper/2601.10770/) (2026)
- [CosyAccent: Duration-Controllable Accent Normalization Using Source-Synthesis Training Data](https://mlsanity.com/paper/2602.19166/) (2026)
- [Synthetic Cross-accent Data Augmentation for Automatic Speech Recognition](https://mlsanity.com/paper/2303.00802/) (2023)
- [Streaming Non-Autoregressive Model for Accent Conversion and Pronunciation Improvement](https://mlsanity.com/paper/2506.16580/) (2025)
- [StyleStream: Real-Time Zero-Shot Voice Style Conversion](https://mlsanity.com/paper/2602.20113/) (2026)
- [Controllable Accent Normalization via Discrete Diffusion](https://mlsanity.com/paper/2603.14275/) (2026)
- [Vibrato Expression Control for Singing Voice Conversion with Improving Independent Control](https://mlsanity.com/paper/2606.17126/) (2026)
- [TokAN: Accent Normalization Using Self-Supervised Speech Tokens](https://mlsanity.com/paper/2607.03928/) (2026)
- [A survey of AI-generated voices and their detection](https://mlsanity.com/paper/2608.15411/) (2026)

## Abstract (arXiv)

Most people who have tried to learn a foreign language would have experienced difficulties understanding or speaking with a native speaker's accent. For native speakers, understanding or speaking a new accent is likewise a difficult task. An accent conversion system that changes a speaker's accent but preserves that speaker's voice identity, such as timbre and pitch, has the potential for a range of applications, such as communication, language learning, and entertainment. Existing accent conversion models tend to change the speaker identity and accent at the same time. Here, we use adversarial learning to disentangle accent dependent features while retaining other acoustic characteristics. What sets our work apart from existing accent conversion models is the capability to convert an unseen speaker's utterance to multiple accents while preserving its original voice identity. Subjective evaluations show that our model generates audio that sound closer to the target accent and like the original speaker.