Toward Application to General Conversation Detection of Dementia Tendency from Conversation Based on Linguistic and Time Features of Speech

Currently, MRI examinations and neuropsychological tests by physicians and clinical psychologists are used to screen for dementia, but they are problematic because they overwhelm medical resources and are highly invasive to patients. If automatic detection of dementia from conversations becomes feasible, it will reduce the burden on medical institutions and realize a less invasive screening method. In this paper, we constructed a machine learning model to identify dementia by extracting linguistic features and time features from the elderly corpus with a control group. Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR) were used in the model. We compared the AUC of the single topic model and the general topic model in three cases: (I) All Features, (II) Gini Impurity, and (III) PCA + Gini Impurity. The AUC of the model constructed using RF in (III) for a single topic was 0.91, showing higher AUC than in the previous study. Furthermore, topic analysis showed that topics with high similarity in utterance content are effective in identifying MCI. In the case of the general topic, the model with AUC of 0.8 showed a high identification performance for unknown topics by cross validation on a topic-by-topic basis, indicating that the general topic model developed in this study can be applied to general conversation.