Scalable Controllable Accented TTS

Henry Li Xinyuan; Zexin Cai; Ashi Garg; Kevin Duh; Leibny Paola García-Perera; Sanjeev Khudanpur; Nicholas Andrews; Matthew Wiesner

arXiv · 2508.07426

Scalable Controllable Accented TTS

Henry Li Xinyuan·Zexin Cai·Ashi Garg·Kevin Duh·Leibny Paola García-Perera·Sanjeev Khudanpur·Nicholas Andrews·Matthew Wiesner

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Abstract

We tackle the challenge of scaling accented TTS systems, expanding their capabilities to include much larger amounts of training data and a wider variety of accent labels, even for accents that are poorly represented or unlabeled in traditional TTS datasets. To achieve this, we employ two strategies: 1. Accent label discovery via a speech geolocation model, which automatically infers accent labels from raw speech data without relying solely on human annotation; 2. Timbre augmentation through kNN voice conversion to increase data diversity and model robustness. These strategies are validated on CommonVoice, where we fine-tune XTTS-v2 for accented TTS with accent labels discovered or enhanced using geolocation. We demonstrate that the resulting accented TTS model not only outperforms XTTS-v2 fine-tuned on self-reported accent labels in CommonVoice, but also existing accented TTS benchmarks.

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BibTeX RIS

Henry Li Xinyuan, Zexin Cai, Ashi Garg, Kevin Duh, Leibny Paola García-Perera, Sanjeev Khudanpur, Nicholas Andrews, Matthew Wiesner. 2025-08-10. Scalable Controllable Accented TTS. https://arxiv.org/abs/2508.07426

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