Parkinson's disease detection from speech using single frequency filtering cepstral coefficients

Sudarsana Kadiri, Rashmi Kethireddy, Paavo Alku

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

1 Citation (Scopus)
27 Downloads (Pure)

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 languageEnglish
Title of host publicationProceedings of Interspeech
PublisherInternational Speech Communication Association
Pages4971-4975
Number of pages5
Volume2020-October
DOIs
Publication statusPublished - 2020
MoE publication typeA4 Article in a conference publication
EventInterspeech - Shanghai, China
Duration: 25 Oct 202029 Oct 2020
Conference number: 21
http://www.interspeech2020.org/

Publication series

NameInterspeech
ISSN (Electronic)1990-9772

Conference

ConferenceInterspeech
Abbreviated titleINTERSPEECH
CountryChina
CityShanghai
Period25/10/202029/10/2020
Internet address

Keywords

  • Parkinson's Disease Detection
  • Single Frequency Filtering
  • Speech Pathology

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