AccentBox: Towards High-Fidelity Zero-Shot Accent Generation
Jinzuomu Zhong, Korin Richmond, Zhiba Su, Siqi Sun
Abstract
While recent Zero-Shot Text-to-Speech (ZS-TTS) models have achieved high naturalness and speaker similarity, they fall short in accent fidelity and control. To address this issue, we propose zero-shot accent generation that unifies Foreign Accent Conversion (FAC), accented TTS, and ZS-TTS, with a novel two-stage pipeline. In the first stage, we achieve state-of-the-art (SOTA) on Accent Identification (AID) with 0.56 f1 score on unseen speakers. In the second stage, we condition a ZS-TTS system on the pretrained speaker-agnostic accent embeddings extracted by the AID model. The proposed system achieves higher accent fidelity on inherent/cross accent generation, and enables unseen accent generation.
BibTeX
@inproceedings{icassp2025_accentboxtowards,
title = {AccentBox: Towards High-Fidelity Zero-Shot Accent Generation},
author = {Jinzuomu Zhong and Korin Richmond and Zhiba Su and Siqi Sun},
booktitle = {ICASSP 2025},
year = {2025}
}