Machine learning model for discrimination of mild dementia patients using acoustic features
Kazu Nishikawa, Akihiro Kuwahara, Rin Hirakawa, Hideaki Kawano, Yoshihisa Nakatoh
Abstract
In previous research on dementia discrimination by voice, a method using multiple acoustic features by machine learning has been proposed. However, they do not focus on speech analysis in mild dementia patients (MCI). Therefore, we propose a dementia discrimination system based on the analysis of vowel utterance features. The analysis results indicated that some cases of dementia appeared in the voice of mild dementia patients. These results can also be used as an index for future improvement of speech sounds in dementia. Taking advantage of these results, we propose an ensemble discrimination system using a classifier with statistical acoustic features and a Neural Network of transformer models, and the F-score is 0.907, which is better than the state-of-the-art methods.