Accent Vector: Controllable Accent Manipulation for Multilingual TTS Without Accented Data

Accent conversion and controllability remain fundamental challenges in cross-lingual text-to-speech (TTS), particularly for low-resource and phonetically diverse Indic languages. While recent large language model (LLM)-based TTS systems exhibit strong cross-lingual generalization, they provide limited explicit control over accent characteristics and intensity. In this paper, we propose CrossAccentTTS, a framework that enables both accent control and conversion while preserving speaker identity. Specifically, we introduce an Accent Intensity Controller (AIC) that injects weighted language embeddings into the accent subspace, allowing smooth interpolation between accents and fine-grained modulation of accent strength at inference time. Experiments on the Indic Multilingual and L2-arctic datasets shows that CrossAccent-TTS achieves precise control of accent intensity, outperforming strong baselines in accent similarity and controllability by maintaining speaker similarity and naturalness.

Accent plays a crucial role in speaker identity and inclusivity in speech technologies. Existing accented text-to-speech (TTS) systems either require large-scale accented datasets or lack fine-grained phoneme-level controllability. We propose a accented TTS framework that combines phonological rules with a multilingual TTS model. The rules are applied to phoneme sequences to transform accent at the phoneme level while preserving intelligibility. The method requires no accented training data and enables explicit phoneme-level accent manipulation. We design rule sets for Spanish- and Indian-accented English, modeling systematic differences in consonants, vowels, and syllable structure arising from phonotactic constraints. We analyze the trade-off between phoneme-level duration alignment and accent as realized in speech timing. Experimental results demonstrate effective accent shift while maintaining speech quality.

Accent text-to-speech (TTS) aims to synthesize speech with a target accent while preserving speaker identity, but faces two key challenges: disentangling accent from speaker characteristics and effectively conditioning speech generation on the two disentangled factors. In this paper, we propose Joycent, a diffusion-based accent TTS framework that addresses both challenges. Our key idea is to separate accent and speaker information in both representation learning and TTS conditioning. Joycent uses WhisAID, a Whisper-based accent encoder with gradient reversal to learn speaker-disentangled accent representations, and introduces layer-specific conditional layer normalization to inject accent and speaker information at different stages of the text encoder. We evaluate Joycent on the Mandarin Regional Accent Corpus (MRAC) with seen and unseen speakers, including a challenging cross-accent setting where the speaker and accent prompts come from different accents. Experimental results show that Joycent improves accent similarity over existing methods while maintaining strong speaker similarity, with consistent gains under the challenging cross-accent setting. Subjective evaluation further confirms improved naturalness, accent similarity, and speaker preservation. The audio samples are available at https://oshindow.github.io/joycent/.