%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{Purohit_ICASSP_2025,
                      author = {Purohit, Tilak and Ruvolo, Barbara and Orozco-Arroyave, Juan Rafael and Magimai-Doss, Mathew},
                    keywords = {fine-tuning, Foundation Models, LoRA, Parkinson’s disease, PC-GITA, Peft, Speech for health},
                    projects = {EMIL},
                       month = apr,
                       title = {Automatic Parkinson’s disease detection from speech: Layer selection vs adaptation of foundation models},
                   booktitle = {Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
                        year = {2025},
                   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.},
                         pdf = {https://publications.idiap.ch/attachments/papers/2025/Purohit_ICASSP_2025.pdf}
}