Automatic Parkinson's disease detection from speech: Layer selection vs adaptation of foundation models
| Type of publication: | Conference paper |
| Citation: | Purohit_ICASSP_2025 |
| Booktitle: | Proceedings of IEEE International Conference Acoustics, Speech, and Signal Processing (ICASSP) |
| Year: | 2025 |
| Abstract: | In this work, we investigate Speech Foundation Models (SFMs) for Parkinson’s Disease (PD) detection. We explore two main approaches: using SFMs as frozen feature extractors and fine-tuning/adapting them for PD detection tasks. 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-cost efficient alternative. The highest accuracy was achieved by fine-tuning Whisper with LoRA. |
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EMIL |
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| Added by: | [UNK] |
| Total mark: | 0 |
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