%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{Hovsepyan_ICASSP_2024,
         author = {Hovsepyan, Sevada and Magimai.-Doss, Mathew},
       keywords = {Classification, language disorder, Parkinson's disease, SVM, syllable-level-features},
       projects = {EMIL},
          title = {SYLLABLE LEVEL FEATURES FOR PARKINSON'S DISEASE DETECTION FROM SPEECH},
      booktitle = {ICASSP},
           year = {2024},
       abstract = {Early detection of Parkinson's disease (PD), one of the most common neurodegenerative diseases, is crucial for successful treatment and symptom management. In this study, we propose a novel approach inspired by neurocomputational models of speech perception, for PD detection from speech samples. Our proposal emphasises the importance of acoustic/linguistic markers to extract features at the syllable level, in contrast to conventional methods that extract features at the frame or state level. Through the use of syllable-level features (SLF), we successfully identify PD in recorded speech samples. Remarkably, the results not only match but potentially exceed the effectiveness of traditional feature sets used for this purpose. We hope that the proposed approach will provide a new basis for integrating linguistic insights into the identification of speech-related diseases.},
            pdf = {https://publications.idiap.ch/attachments/papers/2024/Hovsepyan_ICASSP_2024.pdf}
}