著者

加藤 昇平 鈴木 祐太 小林 朗子 小島 敏昭 伊藤 英則 本間 昭

出版者

一般社団法人 人工知能学会

雑誌

人工知能学会論文誌 (ISSN:13460714)

巻号頁・発行日

vol.26, no.2, pp.347-352, 2011 (Released:2011-01-06)

参考文献数

15

被引用文献数

3 3

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This paper presents a new trial approach to early detection of cognitive impairment in the elderly with the use of speech sound analysis and multivariate statistical technique. In this paper, we focus on the prosodic features from speech sound. Japanese 115 subjects (32 males and 83 females between ages of 38 and 99) participated in this study. We collected speech sound in a few segments of dialogue of HDS-R examination. The segments corresponds to speech sound that is answering for questions on time orientation and number backward count. Firstly, 130 prosodic features have been extracted from each of the speech sounds. These prosodic features consist of spectral and pitch features (53), formant features (56), intensity features (19), and speech rate and response time (2). Secondly, these features are refined by principal component analysis and/or feature selection. Lastly, we have calculated speech prosody-based cognitive impairment rating (SPCIR) by multiple linear regression analysis. The results indicated that there is moderately significant correlation between HDS-R score and synthesis of several selected prosodic features. Consequently, adjusted coefficient of determination R2=0.50 suggests that prosody-based speech sound analysis has possibility to screen the elderly with cognitive impairment.