Clinical assessment and interpretation of dysarthria in ALS using attention based deep learning AI models.

ABSTRACT: Speech dysarthria is a key symptom of neurological conditions like ALS, yet existing AI models designed to analyze it from audio signal rely on handcrafted features with limited inference performance. Deep learning approaches improve accuracy but lack interpretability. We propose an attention-based deep learning AI model to assess dysarthria severity based on listener effort ratings. Using 2,102 recordings from 125 participants, rated by three speech-language pathologists on a 100-point scale, we trained models directly from recordings collected remotely. Our best model achieved R² of 0.92 and RMSE of 6.78. Attention-based interpretability identified key phonemes, such as vowel sounds influenced by 'r' (e.g., "car," "more"), and isolated inspiration sounds as markers of speech d

SUBMITTER: Merler M 

PROVIDER: S-EPMC12062206 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature