%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},
       projects = {EMIL},
          title = {Automatic Parkinson's disease detection from speech: Layer selection vs adaptation of foundation models},
      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.},
            pdf = {https://publications.idiap.ch/attachments/papers/2025/Purohit_ICASSP_2025.pdf}
}