Severity Classification of Parkinson's Disease from Speech using Single Frequency Filtering-based Features
This work addresses the need for improved diagnosis and treatment of Parkinson's disease, but it is incremental as it builds on existing methods with specific feature enhancements.
This study tackled the problem of objectively assessing Parkinson's disease severity from speech by proposing novel features derived from single frequency filtering, which outperformed conventional MFCCs with relative improvements of up to 7.0% across different speaking tasks.
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.