Adapting Self-Supervised Speech Representations for Cross-lingual Dysarthria Detection in Parkinson's Disease
Abstract
The limited availability of dysarthric speech data makes cross-lingual detection an important but challenging problem. A key difficulty is that speech representations often encode language-dependent structure that can confound dysarthria detection. We propose a representation-level language shift (LS) that aligns source-language self-supervised speech representations with the target-language distribution using centroid-based vector adaptation estimated from healthy-control speech. We evaluate the approach on oral DDK recordings from Parkinson's disease speech datasets in Czech, German, and Spanish under both cross-lingual and multilingual settings. LS substantially improves sensitivity and F1 in cross-lingual settings, while yielding smaller but consistent gains in multilingual settings. Representation analysis further shows that LS reduces language identity in the embedding space, supporting the interpretation that LS removes language-dependent structure.
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 main contribution of this paper is the introduction of a centroid-based language shift method that enhances cross-lingual dysarthria detection by aligning self-supervised speech representations. This work represents a meaningful advancement in the field of speech processing, particularly in addressing the challenges posed by language-dependent variations in dysarthric speech.
Methodology
The paper introduces a novel centroid-based language shift (LS) method for adapting self-supervised speech representations to improve cross-lingual dysarthria detection. This approach is innovative as it leverages existing self-supervised models without requiring retraining, which is particularly advantageous in clinical settings where labeled data is scarce. The methodology is well-structured, utilizing centroid-based vector arithmetic to align representations from different languages, thereby addressing the challenge of language-dependent structure in speech data. The authors effectively justify their approach by linking it to the geometric properties of self-supervised representations.
Results & evaluation
The experiments are comprehensive, utilizing multiple datasets from different languages (Czech, German, and Spanish) to evaluate the proposed method under both cross-lingual and multilingual settings. The results demonstrate significant improvements in sensitivity and F1 scores, particularly in cross-lingual scenarios where target-language data is unavailable. The use of logistic regression with 5-fold stratified cross-validation adds rigor to the evaluation, and the inclusion of various metrics (sensitivity, specificity, F1-score) provides a well-rounded assessment of the model's performance.
Reproducibility
The paper provides sufficient detail regarding the experimental setup, including the data preprocessing steps, model training procedures, and evaluation metrics. However, the lack of a publicly available code repository or demo limits the reproducibility of the results. Future work should consider sharing the implementation to facilitate validation and further research.
Limitations
One notable limitation is the reliance on datasets that are drawn from different sources, which may introduce confounding factors that complicate the interpretation of results. Additionally, the focus on oral DDK recordings, while controlled, may not fully capture the complexities of dysarthric speech in more naturalistic settings. The authors acknowledge this limitation and suggest that further research should explore less controlled speech tasks.
Broader impact
The findings have significant implications for the development of cross-lingual speech processing systems, particularly in clinical applications for detecting dysarthria in patients with Parkinson's disease. By improving the accuracy of dysarthria detection across languages, this work could enhance diagnostic capabilities and support more effective interventions for affected individuals globally. The approach could also inspire further research into language adaptation techniques in other areas of speech processing. The main contribution of this paper is the introduction of a centroid-based language shift method that enhances cross-lingual dysarthria detection by aligning self-supervised speech representations. This work represents a meaningful advancement in the field of speech processing, particularly in addressing the challenges posed by language-dependent variations in dysarthric speech.
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