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arXiv · 2611.08972

AccentCanvas: Activity-Guided Bidirectional Accent Conversion

Jaehun Kim·Kang Zhang·Sung Woo Kim·Junmo Kim·Joon Son Chung
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Abstract

Accent conversion for dubbing must change pronunciation while preserving content, voice, and source timing. Normalization alone cannot produce requested non-native accents, and matching total duration does not ensure local speech/pause alignment. We present AccentCanvas, a shared discrete diffusion model for normalization and L1-to-L2 conversion, trained on cross-paired shared-prompt recordings and synthetic pairs. Frame-level speech activity and repetition-preserving units guide local timing through synthesis. Across both tasks, AccentCanvas achieves the highest accent conversion performance (Accent Arrival) and mean listener-rated accent similarity among compared systems, with over 73% lower speech-boundary error than the best baseline for each task. These gains accompany higher L1-to-L2 WER than TokAN and Vevo-Style, highlighting a tradeoff between requested-accent resemblance and ASR-based content recognition. https://smokedindia.github.io/accentcanvas/

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Jaehun Kim, Kang Zhang, Sung Woo Kim, Junmo Kim, Joon Son Chung. 2026-11-06. AccentCanvas: Activity-Guided Bidirectional Accent Conversion. https://arxiv.org/abs/2611.08972

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