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

Sudarsana Kadiri, Manila Kodali, Paavo Alku

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

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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.

Original languageEnglish
Title of host publicationProceedings of Interspeech'23
PublisherInternational Speech Communication Association (ISCA)
Number of pages5
Publication statusPublished - 2023
MoE publication typeA4 Conference publication
EventInterspeech - Dublin, Ireland
Duration: 20 Aug 202324 Aug 2023

Publication series

PublisherInternational Speech Communication Association
ISSN (Electronic)2958-1796




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