Automatic Parkinson?s disease detection from speech: Layer selection vs adaptation of foundation models
| Type of publication: | Conference paper |
| Citation: | Purohit_ICASSP_2025 |
| Publication status: | Accepted |
| Booktitle: | Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) |
| Year: | 2025 |
| Month: | April |
| Publisher: | IEEE |
| Location: | Hyderabad, India |
| 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. |
| Keywords: | fine-tuning, Foundation Models, LoRA, Parkinson’s disease, PC-GITA, Peft, Speech for health |
| Projects |
EMIL |
| Authors | |
| Added by: | [UNK] |
| Total mark: | 0 |
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