Severity Classification of Parkinson's Disease from Speech using Single Frequency Filtering-based Features

TL;DR

This paper introduces novel speech features based on single frequency filtering for classifying Parkinson's disease severity, demonstrating improved accuracy over traditional methods across multiple speaking tasks.

Contribution

It proposes two new feature sets derived from SFF that enhance PD severity classification accuracy compared to conventional MFCCs.

Findings

SFF-based features outperform MFCCs in all speaking tasks.

Relative improvements of up to 7% in classification accuracy.

SFF features provide greater spectro-temporal resolution for PD assessment.

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 spectro-temporal 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% and 2.3% for the vowel task, 7.0% & 1.8% for the sentence task, and 2.4% and 1.1% for the read text task, in comparison to MFCC features.