arXiv:2606.18645v1 Announce Type: cross Abstract: Dysarthria is a speech disorder marked by reduced intelligibility and communicative effectiveness. Automatic utterance-level assessment of dysarthric speech can support scalable speech monitoring and therapy-related analysis. Yet training such systems is bottlenecked by the scarcity of clinically annotated dysarthric speech. This work proposes to augment dysarthric speech assessment using data from speech synthesis evaluations, specifically human-annotated utterances with Mean Opinion Score (MOS) labels from the QualiSpeech corpus. Experiments
The scarcity of annotated clinical speech data is driving innovation in AI training methods, leveraging existing resources like speech synthesis evaluations.
This development could democratize access to advanced speech assessment tools for dysarthria, improving patient monitoring and therapy outcomes through scalable AI.
AI systems for dysarthric speech assessment can now be more effectively trained and deployed, potentially reducing reliance on extensive clinical annotations.
- · Speech pathologists
- · Patients with dysarthria
- · AI developers in healthcare
- · Speech synthesis research
- · Manual speech annotation services
- · Traditional clinical assessment methods
Improved accuracy and accessibility of AI-driven dysarthric speech assessment tools.
Faster development and deployment of AI solutions for other rare medical speech conditions due to transferable methods.
Enhanced quality of life and communication for individuals with dysarthria, integrating AI into their daily medical management.
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Read at arXiv cs.AI