CosyAccent: Duration-Controllable Accent Normalization Using Source-Synthesis Training Data
Abstract
Accent normalization (AN) systems often struggle with unnatural outputs and undesired content distortion, stemming from both suboptimal training data and rigid duration modeling. In this paper, we propose a "source-synthesis" methodology for training data construction. By generating source L2 speech and using authentic native speech as the training target, our approach avoids learning from TTS artifacts and, crucially, requires no real L2 data in training. Alongside this data strategy, we introduce CosyAccent, a non-autoregressive model that resolves the trade-off between prosodic naturalness and duration control. CosyAccent implicitly models rhythm for flexibility yet offers explicit control over total output duration. Experiments show that, despite being trained without any real L2 speech, CosyAccent achieves significantly improved content preservation and superior naturalness compared to strong baselines trained on real-world data.
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 CosyAccent, a novel duration-controllable accent normalization model that utilizes a unique source-synthesis training data strategy to improve the naturalness and content preservation of accent conversion systems. This work represents a meaningful advancement in the field, addressing critical challenges in accent normalization and offering a scalable solution for future research and applications.
Methodology
The paper introduces a novel "source-synthesis" methodology for constructing training data, which synthesizes L2 source speech from a high-quality L1 corpus, thereby avoiding TTS artifacts. This innovative approach allows the model to be trained without real L2 data, which is a significant advancement in the field of accent normalization. The CosyAccent model itself is a non-autoregressive architecture that effectively balances prosodic naturalness and explicit duration control, addressing a critical limitation in existing models.
Results & evaluation
The experiments are well-structured, comparing CosyAccent against strong baselines trained on real L2 data. The results demonstrate significant improvements in content preservation and naturalness, validated through both subjective and objective metrics. The use of a diverse dataset covering multiple accents adds robustness to the evaluation, although the paper could benefit from a more extensive discussion on the statistical significance of the results.
Reproducibility
The paper provides adequate details regarding the model architecture, training data construction, and evaluation metrics, which supports reproducibility. The inclusion of a GitHub repository for code and data further enhances the potential for other researchers to replicate the findings.
Limitations
The primary limitation noted is the model's robustness to acoustic noise and control over paralinguistic features, as the synthetic data used for training is very clean. Additionally, the reliance on a specific TTS model for data generation may limit the generalizability of the approach to other languages or accents.
Broader impact
The implications of this research are significant, particularly for applications in language learning, dubbing, and personalized TTS systems. By reducing the dependency on real L2 data, the proposed method could facilitate the development of accent normalization systems in resource-scarce languages, potentially broadening access to language education and media. The paper presents CosyAccent, a novel duration-controllable accent normalization model that utilizes a unique source-synthesis training data strategy to improve the naturalness and content preservation of accent conversion systems. This work represents a meaningful advancement in the field, addressing critical challenges in accent normalization and offering a scalable solution for future research and applications.
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