Controllable Accent Normalization via Discrete Diffusion
Existing accent normalization methods do not typically offer control over accent strength, yet many applications-such as language learning and dubbing-require tunable accent retention. We propose DLM-AN, a controllable accent normalization system built on masked discrete diffusion over self-supervised speech tokens. A Common Token Predictor identifies source tokens that likely encode native pronunciation; these tokens are selectively reused to initialize the reverse diffusion process. This provides a simple yet effective mechanism for controlling accent strength: reusing more tokens preserves more of the original accent. DLM-AN further incorporates a flow-matching Duration Ratio Predictor...
| Authors | Qibing Bai, Yuhan Du, Tom Ko et al. |
|---|---|
| Primary category | eess.AS |
| Published | 15 March 2026 |
| Last updated at source | 2 October 2026 |
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