Something from Nothing: Data Augmentation for Robust Severity Level Estimation of Dysarthric Speech
arXiv:2603.15988v3 Announce Type: replace-cross Abstract: Dysarthric speech quality assessment (DSQA) is critical for clinical diagnostics and inclusive speech technologies. However, subjective evaluation is costly and difficult to scale, and the scarcity of labeled data limits robust objective modeling. To address this, we propose a three-stage framework that leverages unlabeled dysarthric speech and large-scale typical speech datasets to scale training. A teacher model first generates pseudo-labels for unlabeled samples, followed by weakly supervised pretraining using a label-aware contrasti
The scarcity of specialized medical speech data has long hindered robust objective modeling for conditions like dysarthria, but advances in AI, particularly pseudo-labeling and weakly supervised pretraining, are now enabling scalable solutions.
Improving the objectivity and scalability of dysarthric speech quality assessment can significantly enhance clinical diagnostics and the development of inclusive speech technologies, reducing reliance on expensive subjective evaluations.
The proposed three-stage framework offers a path to overcome data scarcity in medical AI applications by leveraging unlabeled data and large-scale typical speech datasets, leading to more robust and accessible diagnostic tools.
- · AI researchers in medical speech analysis
- · Healthcare providers
- · Patients with dysarthria
- · Developers of inclusive speech technologies
- · Traditional subjective assessment methods for dysarthria
More accurate and accessible diagnostic tools for speech disorders become available, improving patient care.
The methodology could be extended to other medical conditions facing similar data scarcity, accelerating AI integration in diverse healthcare fields.
This could lead to a broader paradigm shift in medical diagnostics, moving towards AI-driven, scalable, and objective assessment across specialties, potentially reducing healthcare costs and improving global access.
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Read at arXiv cs.AI