A Study on Voice Measures in Patients with Parkinson’s Disease

Abstract

This research aims to identify acoustic features which can distinguish patients with Parkinson's disease (PD patients) and healthy speakers. Thirty PD patients and 30 healthy speakers were recruited in the experiment, and their speech was collected, including three vowels (/i/, /a/, and /u/) and nine consonants (/p/, /pʰ/, /t/, /tʰ/, /k/, /kʰ/, /l/, /m/, and /n/). Acoustic features like fundamental frequency (F0), Jitter, Shimmer, harmonics-to-noise ratio (HNR), first formant (F1), second formant (F2), third formant (F3), first bandwidth (B1), second bandwidth (B2), third bandwidth (B3), voice onset, voice onset time were analyzed in our experiment. Two-sample independent t test and the nonparametric Mann-Whitney U (MWU) test were carried out alternatively to compare the acoustic measures between the PD patients and healthy speakers. In addition, after figuring out the effective acoustic features for distinguishing PD patients and healthy speakers, we adopted two methods to detect PD patients: (1) Built classifiers based on the effective acoustic featuresand (2) Trained support vector machine classifiers via the effective acoustic features. Significant differences were found between the male PD group and the male health control in vowel /i/ (Jitter and Shimmer) and /a/ (Shimmer and HNR). Among female subjects, significant differences were observed in F0 standard deviation (F0 SD) of /u/ between the two groups. Additionally, significant differences between PD group and health control were also found in the F3 of /i/ and /n/, whereas other acoustic features showed no significant differences between the two groups. The HNR of vowel /a/ performed the best classification accuracy compared with the other six acoustic features above found to distinguish PD patients and healthy speakers. PD can cause changes in the articulation and phonation of PD patients, wherein increases or decreases occur in some acoustic features. Therefore, the use of acoustic features to detect PD is expected to be a low-cost and large-scale diagnostic method.