Does Language Matter for Early Detection of Parkinson's Disease from Speech?
This research addresses early detection of Parkinson's disease for medical applications, but it is incremental as it builds on existing speech analysis methods.
The study tackled the problem of detecting Parkinson's disease from speech by assessing the role of language, finding that text-only models match vocal-feature models and multilingual Whisper outperforms self-supervised models.
Using speech samples as a biomarker is a promising avenue for detecting and monitoring the progression of Parkinson's disease (PD), but there is considerable disagreement in the literature about how best to collect and analyze such data. Early research in detecting PD from speech used a sustained vowel phonation (SVP) task, while some recent research has explored recordings of more cognitively demanding tasks. To assess the role of language in PD detection, we tested pretrained models with varying data types and pretraining objectives and found that (1) text-only models match the performance of vocal-feature models, (2) multilingual Whisper outperforms self-supervised models whereas monolingual Whisper does worse, and (3) AudioSet pretraining improves performance on SVP but not spontaneous speech. These findings together highlight the critical role of language for the early detection of Parkinson's disease.