Speech Foundation Models for Parkinson’s Disease Detection: A Layer-Wise Comparison with Handcrafted Acoustics Across Two Cohorts

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Abstract Foundation models have transformed speech processing, but their value for clinical voice analysis, particularly in Parkinson’s disease (PD), remains largely unexplored. In this study, we compare layer-wise representations from several foundation-model families (Whisper, Wav2Vec 2.0, OmniASR, NVIDIA Nemotron, and Qwen3-ASR) with a handcrafted acoustic baseline using sentence reading and sustained vowels from independent cohorts (PC-GITA and NeuroVoz). On sentence reading, the best foundation-model configurations achieved segment-level F1-scores of approximately 0.88 in both cohorts, above the handcrafted acoustic baseline. Their advantage on sustained vowels was smaller and more cohort-dependent, with overlapping uncertainty in participant-level comparisons. Soft-voting aggregation generally improved participant-level discrimination. Intermediate-to-late encoder layers most often yielded the strongest representations, although optimal depth varied by architecture and task. Stratified analyses revealed cohort disparities, F1-scores were higher for male speakers in PC-GITA and for senior speakers in NeuroVoz, so foundation-model representations alone do not remove demographic performance gaps. Overall, these results support speech foundation models as promising feature extractors for PD detection and motivate validation in larger clinical cohorts.

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