This work is concerned with devising a robust Parkinson’s (PD) disease detector from speech in real-world operating conditions using (i) foundational models, and (ii) speech enhancement (SE) methods. To this end, we first fine-tune several foundational-based models on the standard PC-GITA (s-PCGITA) clean data. Our results demonstrate superior performance to previously proposed models. Second, we assess the generalization capability of the PD models on the extended PCGITA (e-PC-GITA) recordings, collected in real-world operative conditions, and observe a severe drop in performance moving from ideal to real-world conditions. Third, we align training and testing conditions applaying off-the-shelf SE techniques on e-PC-GITA, and a significant boost in performance is observed only for the foundational-based models. Finally, combining the two best foundational-based models trained on s-PCGITA, namely WavLM Base and Hubert Base, yielded top performance on the enhanced e-PC-GITA
La Quatra M., Turco M.F., Svendsen T., Salvi G., Orozco-Arroyave J.R., Siniscalchi S.M. (2024). Exploiting Foundation Models and Speech Enhancement for Parkinson's Disease Detection from Speech in Real-World Operative Conditions. In INTERSPEECH (pp. 1405-1409) [10.21437/Interspeech.2024-522].
Exploiting Foundation Models and Speech Enhancement for Parkinson's Disease Detection from Speech in Real-World Operative Conditions
La Quatra M.Formal Analysis
;Siniscalchi S. M.
Conceptualization
2024-09-01
Abstract
This work is concerned with devising a robust Parkinson’s (PD) disease detector from speech in real-world operating conditions using (i) foundational models, and (ii) speech enhancement (SE) methods. To this end, we first fine-tune several foundational-based models on the standard PC-GITA (s-PCGITA) clean data. Our results demonstrate superior performance to previously proposed models. Second, we assess the generalization capability of the PD models on the extended PCGITA (e-PC-GITA) recordings, collected in real-world operative conditions, and observe a severe drop in performance moving from ideal to real-world conditions. Third, we align training and testing conditions applaying off-the-shelf SE techniques on e-PC-GITA, and a significant boost in performance is observed only for the foundational-based models. Finally, combining the two best foundational-based models trained on s-PCGITA, namely WavLM Base and Hubert Base, yielded top performance on the enhanced e-PC-GITAFile | Dimensione | Formato | |
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