# MacST: Multi-Accent Speech Synthesis via Text Transliteration for Accent Conversion

arXiv: https://arxiv.org/abs/2409.09352 · HTML: https://mlsanity.com/paper/2409.09352/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2024.

## Built on by (most cited first, 6 of 6)

- [Pairwise Evaluation of Accent Similarity in Speech Synthesis](https://mlsanity.com/paper/2505.14410/) (2025)
- [Quantifying Speaker Embedding Phonological Rule Interactions in Accented Speech Synthesis](https://mlsanity.com/paper/2601.14417/) (2026)
- [CosyAccent: Duration-Controllable Accent Normalization Using Source-Synthesis Training Data](https://mlsanity.com/paper/2602.19166/) (2026)
- [Accent Vector: Controllable Accent Manipulation for Multilingual TTS Without Accented Data](https://mlsanity.com/paper/2603.07534/) (2026)
- [Learning-free L2-Accented Speech Generation using Phonological Rules](https://mlsanity.com/paper/2603.07550/) (2026)
- [Vibrato Expression Control for Singing Voice Conversion with Improving Independent Control](https://mlsanity.com/paper/2606.17126/) (2026)

## Abstract (arXiv)

In accented voice conversion or accent conversion, we seek to convert the accent in speech from one another while preserving speaker identity and semantic content. In this study, we formulate a novel method for creating multi-accented speech samples, thus pairs of accented speech samples by the same speaker, through text transliteration for training accent conversion systems. We begin by generating transliterated text with Large Language Models (LLMs), which is then fed into multilingual TTS models to synthesize accented English speech. As a reference system, we built a sequence-to-sequence model on the synthetic parallel corpus for accent conversion. We validated the proposed method for both native and non-native English speakers. Subjective and objective evaluations further validate our dataset's effectiveness in accent conversion studies.