Controllable Accent Normalization via Discrete Diffusion
Abstract
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 that automatically adjusts the total duration to better match the native rhythm. Experiments on multi-accent English data show that DLM-AN achieves the lowest word error rate among all compared systems while delivering competitive accent reduction and smooth, interpretable accent strength control.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents DLM-AN, a controllable accent normalization system that effectively balances accent retention and content preservation through innovative methodologies. This contribution is significant for advancing the field of speech processing, particularly in applications requiring nuanced accent control.
Methodology
The proposed DLM-AN system introduces a novel approach to accent normalization by leveraging masked discrete diffusion and a Common Token Predictor (CTP) to control accent strength. The methodology effectively combines self-supervised speech tokens with a flow-matching Duration Ratio Predictor, allowing for nuanced control over both accent retention and speech rhythm. The use of a bidirectional Transformer for token prediction and the iterative generation process enhances the model's ability to produce high-quality outputs while maintaining phonetic integrity. However, the reliance on a recognition-based token encoder may introduce errors that could affect performance on heavily accented inputs.
Results & evaluation
The experiments conducted on multi-accent English data demonstrate that DLM-AN achieves the lowest word error rate (WER) among competing systems while maintaining competitive naturalness and accent reduction. The evaluation metrics include both subjective assessments (MUSHRA tests for naturalness and accentedness) and objective measures (WER, Speaker Encoding Cosine Similarity, and phonetic posteriorgram distance), providing a comprehensive view of the system's performance. The results indicate that the proposed method effectively balances accent normalization and content preservation.
Reproducibility
The paper provides a detailed description of the experimental setup, including datasets, training procedures, and evaluation metrics, which enhances reproducibility. However, the lack of a publicly available code repository limits the ability of other researchers to replicate the results fully. The authors mention using specific models and datasets, but without access to the exact implementations, some aspects may be challenging to reproduce.
Limitations
The paper acknowledges several limitations, including the potential for recognition errors in the token encoder, which can degrade conversion quality for heavily accented inputs. Additionally, the current system relies on a K-Means tokenizer, which may not capture the full phonetic richness necessary for optimal performance. Future work could explore incorporating L2-accented data and improving the tokenizer to enhance robustness.
Broader impact
The DLM-AN system has significant implications for applications in language learning, dubbing, and personalized text-to-speech systems, where controllable accent normalization is crucial. By enabling users to adjust accent strength, the system can facilitate better communication and understanding in multilingual contexts. The research contributes to the broader field of speech processing and accent conversion, paving the way for more sophisticated and user-friendly audio technologies. The paper presents DLM-AN, a controllable accent normalization system that effectively balances accent retention and content preservation through innovative methodologies. This contribution is significant for advancing the field of speech processing, particularly in applications requiring nuanced accent control.
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