Accent Normalization Using Self-Supervised Discrete Tokens with Non-Parallel Data

Qibing Bai; Sho Inoue; Shuai Wang; Zhongjie Jiang; Yannan Wang; Haizhou Li

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arXiv · 2507.17735

Accent Normalization Using Self-Supervised Discrete Tokens with Non-Parallel Data

Qibing Bai·Sho Inoue·Shuai Wang·Zhongjie Jiang·Yannan Wang·Haizhou Li

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Abstract

Accent normalization converts foreign-accented speech into native-like speech while preserving speaker identity. We propose a novel pipeline using self-supervised discrete tokens and non-parallel training data. The system extracts tokens from source speech, converts them through a dedicated model, and synthesizes the output using flow matching. Our method demonstrates superior performance over a frame-to-frame baseline in naturalness, accentedness reduction, and timbre preservation across multiple English accents. Through token-level phonetic analysis, we validate the effectiveness of our token-based approach. We also develop two duration preservation methods, suitable for applications such as dubbing.

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Qibing Bai, Sho Inoue, Shuai Wang, Zhongjie Jiang, Yannan Wang, Haizhou Li. 2025-07-23. Accent Normalization Using Self-Supervised Discrete Tokens with Non-Parallel Data. https://arxiv.org/abs/2507.17735

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