Automatic Parkinson's disease detection from speech: Layer selection vs adaptation of foundation models
Tilak Purohit, Barbara Ruvolo, Juan Rafael Orozco-Arroyave, Mathew Magimai-Doss
Abstract
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.
BibTeX
@inproceedings{icassp2025_automaticparkins,
title = {Automatic Parkinson's disease detection from speech: Layer selection vs adaptation of foundation models},
author = {Tilak Purohit and Barbara Ruvolo and Juan Rafael Orozco-Arroyave and Mathew Magimai-Doss},
booktitle = {ICASSP 2025},
year = {2025}
}