# Modelling low-resource accents without accent-specific TTS frontend

arXiv: https://arxiv.org/abs/2301.04606 · HTML: https://mlsanity.com/paper/2301.04606/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 41 references that are in the map.
- First posted on arXiv 11 Jan 2023.

## Builds on (most cited first, 12 of 12)

- [Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions](https://mlsanity.com/paper/1712.05884/) (2017)
- [Generalized End-to-End Loss for Speaker Verification](https://mlsanity.com/paper/1710.10467/) (2017)
- [Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotron](https://mlsanity.com/paper/1803.09047/) (2018)
- [Learning to Speak Fluently in a Foreign Language: Multilingual Speech Synthesis and Cross-Language Voice Cloning](https://mlsanity.com/paper/1907.04448/) (2019)
- [Universal Neural Vocoding with Parallel WaveNet](https://mlsanity.com/paper/2102.01106/) (2021)
- [Accent and Speaker Disentanglement in Many-to-many Voice Conversion](https://mlsanity.com/paper/2011.08609/) (2020)
- [Non-Autoregressive TTS with Explicit Duration Modelling for Low-Resource Highly Expressive Speech](https://mlsanity.com/paper/2106.12896/) (2021)
- [Training Text-To-Speech Systems From Synthetic Data: A Practical Approach For Accent Transfer Tasks](https://mlsanity.com/paper/2208.13183/) (2022)
- [Unify and Conquer: How Phonetic Feature Representation Affects Polyglot Text-To-Speech (TTS)](https://mlsanity.com/paper/2207.01547/) (2022)
- [AISPEECH-SJTU accent identification system for the Accented English Speech Recognition Challenge](https://mlsanity.com/paper/2102.09828/) (2021)
- [Mix and Match: An Empirical Study on Training Corpus Composition for Polyglot Text-To-Speech (TTS)](https://mlsanity.com/paper/2207.01507/) (2022)
- [Remap, warp and attend: Non-parallel many-to-many accent conversion with Normalizing Flows](https://mlsanity.com/paper/2211.05850/) (2022)

## Built on by (most cited first, 3 of 3)

- [The Ethical Implications of Generative Audio Models: A Systematic Literature Review](https://mlsanity.com/paper/2307.05527/) (2023)
- [VoiceShop: A Unified Speech-to-Speech Framework for Identity-Preserving Zero-Shot Voice Editing](https://mlsanity.com/paper/2404.06674/) (2024)
- [Complex-Text Robustness Evaluation and Failure Diagnosis for Low-Resource Multilingual Text-to-Speech](https://mlsanity.com/paper/2609.11545/) (2026)

## Abstract (arXiv)

This work focuses on modelling a speaker's accent that does not have a dedicated text-to-speech (TTS) frontend, including a grapheme-to-phoneme (G2P) module. Prior work on modelling accents assumes a phonetic transcription is available for the target accent, which might not be the case for low-resource, regional accents. In our work, we propose an approach whereby we first augment the target accent data to sound like the donor voice via voice conversion, then train a multi-speaker multi-accent TTS model on the combination of recordings and synthetic data, to generate the donor's voice speaking in the target accent. Throughout the procedure, we use a TTS frontend developed for the same language but a different accent. We show qualitative and quantitative analysis where the proposed strategy achieves state-of-the-art results compared to other generative models. Our work demonstrates that low resource accents can be modelled with relatively little data and without developing an accent-specific TTS frontend. Audio samples of our model converting to multiple accents are available on our web page.