# Evaluating Methods for Ground-Truth-Free Foreign Accent Conversion

arXiv: https://arxiv.org/abs/2309.02133 · HTML: https://mlsanity.com/paper/2309.02133/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 40 references that are in the map.
- First posted on arXiv 5 Sep 2023.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions](https://mlsanity.com/paper/1712.05884/) (2017)
- [Libri-Light: A Benchmark for ASR with Limited or No Supervision](https://mlsanity.com/paper/1912.07875/) (2019)
- [vq-wav2vec: Self-Supervised Learning of Discrete Speech Representations](https://mlsanity.com/paper/1910.05453/) (2019)
- [Parallel WaveGAN: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogram](https://mlsanity.com/paper/1910.11480/) (2019)
- [An Overview of Voice Conversion and its Challenges: From Statistical Modeling to Deep Learning](https://mlsanity.com/paper/2008.03648/) (2020)
- [Voice Conversion Challenge 2020: Intra-lingual semi-parallel and cross-lingual voice conversion](https://mlsanity.com/paper/2008.12527/) (2020)
- [The VoiceMOS Challenge 2022](https://mlsanity.com/paper/2203.11389/) (2022)
- [Voice Transformer Network: Sequence-to-Sequence Voice Conversion Using Transformer with Text-to-Speech Pretraining](https://mlsanity.com/paper/1912.06813/) (2019)
- [S3PRL-VC: Open-source Voice Conversion Framework with Self-supervised Speech Representations](https://mlsanity.com/paper/2110.06280/) (2021)
- [Predictions of Subjective Ratings and Spoofing Assessments of Voice Conversion Challenge 2020 Submissions](https://mlsanity.com/paper/2009.03554/) (2020)
- [Pretraining Techniques for Sequence-to-Sequence Voice Conversion](https://mlsanity.com/paper/2008.03088/) (2020)
- [Towards Identity Preserving Normal to Dysarthric Voice Conversion](https://mlsanity.com/paper/2110.08213/) (2021)
- [When is it Better to Compare than to Score?](https://mlsanity.com/paper/1406.6618/) (2014)
- [A Comparative Study of Self-supervised Speech Representation Based Voice Conversion](https://mlsanity.com/paper/2207.04356/) (2022)
- [Non-autoregressive sequence-to-sequence voice conversion](https://mlsanity.com/paper/2104.06793/) (2021)
- [A Preliminary Study of a Two-Stage Paradigm for Preserving Speaker Identity in Dysarthric Voice Conversion](https://mlsanity.com/paper/2106.01415/) (2021)

## Built on by (most cited first, 4 of 4)

- [Improving Pronunciation and Accent Conversion through Knowledge Distillation And Synthetic Ground-Truth from Native TTS](https://mlsanity.com/paper/2410.14997/) (2024)
- [A Pilot Study of Applying Sequence-to-Sequence Voice Conversion to Evaluate the Intelligibility of L2 Speech Using a Native Speaker's Shadowings](https://mlsanity.com/paper/2410.02239/) (2024)
- [Acoustic and perceptual differences between standard and accented speech and their voice clones](https://mlsanity.com/paper/2604.01562/) (2026)
- [TokAN: Accent Normalization Using Self-Supervised Speech Tokens](https://mlsanity.com/paper/2607.03928/) (2026)

## Abstract (arXiv)

Foreign accent conversion (FAC) is a special application of voice conversion (VC) which aims to convert the accented speech of a non-native speaker to a native-sounding speech with the same speaker identity. FAC is difficult since the native speech from the desired non-native speaker to be used as the training target is impossible to collect. In this work, we evaluate three recently proposed methods for ground-truth-free FAC, where all of them aim to harness the power of sequence-to-sequence (seq2seq) and non-parallel VC models to properly convert the accent and control the speaker identity. Our experimental evaluation results show that no single method was significantly better than the others in all evaluation axes, which is in contrast to conclusions drawn in previous studies. We also explain the effectiveness of these methods with the training input and output of the seq2seq model and examine the design choice of the non-parallel VC model, and show that intelligibility measures such as word error rates do not correlate well with subjective accentedness. Finally, our implementation is open-sourced to promote reproducible research and help future researchers improve upon the compared systems.