Previous studies on Italian speech showed that the percentage of vocalic portion in the utterance (%V) and the duration of the interval between two consecutive vowel onset points (VtoV) were larger for parkinsonian (PD) than for healthy controls (HC). Especially, the values of %V were distinctly separated between PD and HC. The present study aimed to further test the finding on Mandarin and Polish. Twenty-five Mandarin speakers (13 PD and 12 HC matched on age) and thirty-one Polish speakers (18 PD and 13 HC matched on age) read aloud a passage of story. The recorded speeches were segmented into vocalic and consonantal intervals, and then %V and VtoV were calculated. For both languages, VtoV overlapped between HC and PD. For Polish, %V was distinctly higher in PD than in HC, while for Mandarin there was no significant difference. It suggests that %V could be used for automatic diagnosis of PD for Italian and Polish, but not for Mandarin. The effectiveness of the rhythmic metric appears to be language-dependent, varying with the rhythmic typology of the language.

To investigate the effectiveness of identifying patients with Parkinson’s disease (PD) from speech signals, various acoustic parameters including prosodic and segmental features are extracted from speech and then the random forest classification (RF) algorithm based on these acoustic parameters is applied to diagnose early-stage PD patients. To validate the proposed method of RF algorithm in early-stage PD identification, this study compares the accuracy rate of RF with that of neurologists’ judgments based on auditory test outcomes, and the results clearly show the superiority of the proposed method over its rival. Random forest algorithm based on speech can improve the accuracy of patients’ identification, which provides an efficient auxiliary method in the early diagnosis of PD patients.

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