CONF Purohit_ICASSP_2025/IDIAP Automatic Parkinson?s disease detection from speech: Layer selection vs adaptation of foundation models Purohit, Tilak Ruvolo, Barbara Orozco-Arroyave, Juan Rafael Magimai-Doss, Mathew fine-tuning Foundation Models LoRA Parkinson’s disease PC-GITA Peft Speech for health EXTERNAL http://publications.idiap.ch/attachments/papers/2025/Purohit_ICASSP_2025.pdf PUBLIC Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) Hyderabad, India 2025 IEEE In this work, we investigate Speech Foundation Models (SFMs) for Parkinson’s Disease (PD) detection. We explore two main approaches: (1) using SFMs as frozen feature extractors and, (2) fine-tuning/adapting SFMs for PD detection. We propose a cross-validation-based layer selection methodology to identify the layer effective for PD detection. Additionally, we compare the performance of the layer selection scheme with full fine-tuning and, parameter-efficient fine-tuning (PEFT) using Low-Rank Adaptation (LoRA). Our results show that layer selection and LoRA-based fine-tuning can perform on par with full fine-tuning, providing a more parameter-efficient alternative. The highest accuracy was achieved by fine-tuning Whisper using LoRA.