Parkinson's disease detection from speech using single frequency filtering cepstral coefficients
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A4 Artikkeli konferenssijulkaisussa
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Date
2020
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Mcode
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Language
en
Pages
5
4971-4975
4971-4975
Series
Proceedings of Interspeech, Interspeech
Abstract
Parkinson's disease (PD) is a progressive deterioration of the human central nervous system. Detection of PD (discriminating patients with PD from healthy subjects) from speech is a useful approach due to its non-invasive nature. This study proposes to use novel cepstral coefficients derived from the single frequency filtering (SFF) method, called as single frequency filtering cepstral coefficients (SFFCCs) for the detection of PD. SFF has been shown to provide higher spectro-temporal resolution compared to the short-time Fourier transform. The current study uses the PC-GITA database, which consists of speech from speakers with PD and healthy controls (50 males, 50 females). Our proposed detection system is based on the i-vectors derived from SFFCCs using SVM as a classifier. In the detection of PD, better performance was achieved when the i-vectors were computed from the proposed SFFCCs compared to the popular conventional MFCCs. Furthermore, we investigated the effect of temporal variations by deriving the shifted delta cepstral (SDC) coefficients using SFFCCs. These experiments revealed that the i-vectors derived from the proposed SFFCCs+SDC features gave an absolute improvement of 9% compared to the i-vectors derived from the baseline MFCCs+SDC features, indicating the importance of temporal variations in the detection of PD.Description
Keywords
Parkinson's Disease Detection, Single Frequency Filtering, Speech Pathology
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Citation
Kadiri, S, Kethireddy, R & Alku, P 2020, Parkinson's disease detection from speech using single frequency filtering cepstral coefficients . in Proceedings of Interspeech . vol. 2020-October, Interspeech, International Speech Communication Association (ISCA), pp. 4971-4975, Interspeech, Shanghai, China, 25/10/2020 . https://doi.org/10.21437/Interspeech.2020-3197