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.
Original language | English |
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Title of host publication | Proceedings of Interspeech |
Publisher | International Speech Communication Association |
Pages | 4971-4975 |
Number of pages | 5 |
Volume | 2020-October |
DOIs | |
Publication status | Published - 2020 |
MoE publication type | A4 Article in a conference publication |
Event | Interspeech - Shanghai, China Duration: 25 Oct 2020 → 29 Oct 2020 Conference number: 21 http://www.interspeech2020.org/ |
Publication series
Name | Interspeech |
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ISSN (Electronic) | 1990-9772 |
Conference
Conference | Interspeech |
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Abbreviated title | INTERSPEECH |
Country/Territory | China |
City | Shanghai |
Period | 25/10/2020 → 29/10/2020 |
Internet address |
Keywords
- Parkinson's Disease Detection
- Single Frequency Filtering
- Speech Pathology