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.

翻译：开发评估帕金森病（PD）严重程度的客观方法对改善诊断和治疗至关重要。本研究提出基于单频滤波（SFF）方法的两组新型特征：（1）SFF倒谱系数（SFFCC）和（2）基于SFF的梅尔频率倒谱系数（MFCC-SFF），用于PD严重程度分类。既往研究表明，与短时傅里叶变换相比，SFF具有更高的时频分辨率。本研究采用PC-GITA数据库，包含PD患者和健康对照组在三种发音任务（元音、语句、文本朗读）中的语音数据。基于SVM分类器的实验表明，所提特征在所有三种发音任务中均优于传统MFCC特征。与MFCC特征相比，所提SFFCC和MFCC-SFF特征在元音任务中分别提升5.8%和2.3%，在语句任务中提升7.0%和1.8%，在文本朗读任务中提升2.4%和1.1%。