Dementia diagnosis requires a series of different testing methods, which is complex and time-consuming. Early detection of dementia is crucial as it can prevent further deterioration of the condition. This paper utilizes a speech recognition model to construct a dementia assessment system tailored for Mandarin speakers during the picture description task. By training an attention-based speech recognition model on voice data closely resembling real-world scenarios, we have significantly enhanced the model's recognition capabilities. Subsequently, we extracted the encoder from the speech recognition model and added a linear layer for dementia assessment. We collected Mandarin speech data from 99 subjects and acquired their clinical assessments from a local hospital. We achieved an accuracy of 92.04% in Alzheimer's disease detection and a mean absolute error of 9% in clinical dementia rating score prediction.
@article{arxiv.2310.03985,
title = {Dementia Assessment Using Mandarin Speech with an Attention-based Speech Recognition Encoder},
author = {Zih-Jyun Lin and Yi-Ju Chen and Po-Chih Kuo and Likai Huang and Chaur-Jong Hu and Cheng-Yu Chen},
journal= {arXiv preprint arXiv:2310.03985},
year = {2023}
}