%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{Hovsepyan_ICASSP2025_2024,
         author = {Hovsepyan, Sevada and Magimai.-Doss, Mathew},
       keywords = {Formants, Parkinson's disease, speech pathology detection, syllables},
       projects = {Idiap, EMIL},
          title = {Parkinson's Disease Detection through Formant and F0 Analysis at Syllable Level},
      booktitle = {International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
           year = {2024},
       abstract = {In a recent publication, we put forth a novel approach to syllable-based feature extraction for the detection of Parkinson's disease. The method entails the calculation of standardised spectrotemporal patterns of syllable-like segments (fixed number of frequency and temporal bins), which are then employed as a feature vector for the detection of Parkinson's disease. As the classification performance based on the syllable-level features increased with the inclusion of more frequency bins, we postulated that the standardised spectrotemporal patterns bear resemblance to, or contain, the formant transitions that have been demonstrated to be altered in Parkinson's disease. In this study, we initially demonstrated that the extraction of syllable-level features based on spectrogram energy under the formant and F0 patterns resulted in a significant improvement in classification outcomes. To further test our hypothesis, we statistically compared the eGeMAPS feature set across conditions. This revealed that features related to fundamental frequency and formants are statistically different between Parkinson's disease and healthy conditions. Taken together, our results suggest that syllable-level, formant-informed feature selection can provide reliable PD detection with a relatively small number of features.},
            pdf = {https://publications.idiap.ch/attachments/papers/2024/Hovsepyan_ICASSP2025_2024.pdf}
}