Affiliations: [a] Department of Neurology and Neuroscience Institute, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| [b] Department of Neurology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| [c] Department of Psychiatry, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Correspondence:
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Correspondence to: Sheng-Di Chen, Ru-Jing Ren, and Gang Wang, Department of Neurology and Neuroscience Institute, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China. E-mails: rjchensd@rjh.com.cn (Chen), doctorren2001@126.com (Ren) and wg11424@rjh.com.cn (Wang).
Note: [1] These authors contributed equally to this work.
Abstract: Background:Language dysfunction is a frequently reported symptom in Alzheimer’s disease (AD). However, computer-assisted analysis of spontaneous speech in AD and mild cognitive impairment (MCI) is rarely used to date. Objective:To characterize the language impairment in AD and amnestic MCI (aMCI) with computer-based automatic analysis via the “Automatic Speech Recognition (ASR) software for cognitive impairment V1.3”. Methods:A total of 64 subjects, including 20 AD patients, 20 aMCI patients, and 24 healthy controls were recruited. All subjects underwent neuropsychological tests, and spontaneous speech samples were recorded through the description of the “Cookie-Theft Picture” and then analyzed by the computerized software. Subsequently, we compared the speech parameters between the subjects and the controls. Results:We identified seven spontaneous speech parameters (percentage of silence duration, average duration of phrasal segments, average duration of silence segments, number of speech segments, number of long pauses, ratio of hesitation/speech counts and ratio of short pause/speech counts) demonstrating significant differences between the three groups (p < 0.05). All seven speech parameters significantly correlated with cognitive performance, with average duration of silence segments demonstrating the best correlation to cognitive performance on stepwise multiple linear regression analysis. Conclusion:Computer-assisted automated analysis of speech/silence segments demonstrated the potential to reflect the intrinsic linguistic impairment associated with MCI and AD. It has a promising prospect in the early detection of AD and assessment of disease severity.
