目的:本研究旨在确定可以区分帕金森病患者(PD患者)和健康说话者的声学特征。
方法:实验招募30名PD患者和30名健康者,他们的演讲被收集了,包括三个元音(/i/,/a/,和/u/)和九个辅音(/p/,/ps/,/t/,/tā/,/k/,/kā/,/l/,/m/,和/n/)。声学特征,如基频(F0),抖动,微光,谐波噪声比(HNR),第一共振峰(F1),第二共振峰(F2),第三共振峰(F3),第一带宽(B1),第二带宽(B2),第三带宽(B3),声音发作,在我们的实验中分析了声音的发作时间。交替进行两样本独立t检验和非参数Mann-WhitneyU(MWU)检验,以比较PD患者和健康说话者之间的声学测量。此外,在找出区分PD患者和健康说话者的有效声学特征后,我们采用了两种方法来检测PD患者:(1)基于有效的声学特征构建分类器;(2)通过有效的声学特征训练支持向量机分类器。
结果:在元音/i/(抖动和闪光)和/a/(闪光和HNR)方面,男性PD组和男性健康对照组之间存在显着差异。在女性受试者中,两组之间的/u/的F0标准差(F0SD)存在显着差异。此外,在/i/和/n/的F3中,PD组和健康对照组之间也存在显着差异,而其他声学特征显示两组之间没有显着差异。与上述发现的区分PD患者和健康说话者的其他六个声学特征相比,元音/a/的HNR表现出最佳的分类准确性。
结论:PD可引起PD患者发音和发音的改变,其中增加或减少发生在一些声学特征中。因此,利用声学特征检测PD有望成为一种低成本、大规模的诊断方法。
OBJECTIVE: This research aims to identify acoustic features which can distinguish patients with Parkinson\'s disease (PD patients) and healthy speakers.
METHODS: 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 features and (2) Trained support vector machine classifiers via the effective acoustic features.
RESULTS: 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.
CONCLUSIONS: 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.