Accent is an integral part of society, reflecting multiculturalism and shaping how individuals express identity. The majority of English speakers are non-native (L2) speakers, yet current Text-To-Speech (TTS) systems primarily model American-accented English due limited accented data. We propose \textit{Accent Vector}, a controllable representation that enables accent manipulation in multilingual TTS without requiring accented training data. \textit{Accent Vector} is derived by fine-tuning a TTS system on native speech of a different language (i.e. non-English) and computing task vectors capturing accent characteristics (i.e. in English). By scaling and interpolating the vector, we achieve fine-grained control over accent strength and generate mixed-accent speech. In addition, it generalizes beyond English, enabling accent control across multiple languages. Objective and human evaluations confirm the effectiveness of Accent Vector for fine-grained and compositional accent control.
@article{arxiv.2603.07534,
title = {Accent Vector: Controllable Accent Manipulation for Multilingual TTS Without Accented Data},
author = {Thanathai Lertpetchpun and Thanapat Trachu and Jihwan Lee and Tiantian Feng and Dani Byrd and Shrikanth Narayanan},
journal= {arXiv preprint arXiv:2603.07534},
year = {2026}
}