Severity classification of Parkinson's disease from speech using single frequency filtering-based features

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A4 Artikkeli konferenssijulkaisussa
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Date
2023
Department
Department of Information and Communications Engineering
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Mcode
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Language
en
Pages
5
2393-2397
Series
Proceedings of Interspeech'23, Volume 2023-August, Interspeech
Abstract
Developing objective methods for assessing the severity of Parkinson's disease (PD) is crucial for improving the diagnosis and treatment. This study proposes two sets of novel features derived from the single frequency filtering (SFF) method: (1) SFF cepstral coefficients (SFFCC) and (2) MFCCs from the SFF (MFCC-SFF) for the severity classification of PD. Prior studies have demonstrated that SFF offers greater spectrotemporal resolution compared to the short-time Fourier transform. The study uses the PC-GITA database, which includes speech of PD patients and healthy controls produced in three speaking tasks (vowels, sentences, text reading). Experiments using the SVM classifier revealed that the proposed features outperformed the conventional MFCCs in all three speaking tasks. The proposed SFFCC and MFCC-SFF features gave a relative improvement of 5.8% & 2.3% for the vowel task, 7.0% & 1.8% for the sentence task, and 2.4% & 1.1% for the read text task, in comparison to MFCC features.
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DOI
10.21437/Interspeech.2023-2531
Citation
Kadiri, S, Kodali, M & Alku, P 2023, Severity classification of Parkinson's disease from speech using single frequency filtering-based features . in Proceedings of Interspeech'23 . vol. 2023-August, Interspeech, International Speech Communication Association (ISCA), pp. 2393-2397, Interspeech, Dublin, Ireland, 20/08/2023 . https://doi.org/10.21437/Interspeech.2023-2531
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https://urn.fi/URN:NBN:fi:aalto-202311296997
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