arXiv:2606.13253v1 Announce Type: cross Abstract: Speech recognition is challenging for dysarthric speakers. While federated learning (FL)-based ASR can be an effective tool for protecting privacy, it suffers from heterogeneity issues caused by speaker variability. Forcing all speakers to share the same model components can be suboptimal under such heterogeneity, making personalization a promising direction; however, related research on dysarthric speech remains limited. To this end, this paper explores two aggregation strategies to achieve personalization, including the parameter-based averag
The increasing maturity of federated learning techniques and the growing interest in personalized AI solutions are converging to address specialized challenges like dysarthric speech recognition.
This development represents a step towards making advanced speech recognition more inclusive and effective for populations with speech impediments, expanding the utility and impact of AI.
The focus shifts from generic federated learning models to personalized approaches within federated settings, recognizing and addressing individual user variations for improved performance.
- · Dysarthric speakers
- · AI accessibility technology developers
- · Healthcare technology sektor
- · Federated learning researchers
- · Generic ASR systems
- · Non-personalized FL models
Improved daily communication and quality of life for individuals with dysarthria via more accurate speech recognition.
Expansion of federated learning applications into sensitive healthcare domains, driven by privacy-preserving personalized models.
Reduced social and economic barriers for people with communication challenges, fostering greater inclusion in digital society.
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Read at arXiv cs.AI