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.

通过训练一个基于注意力的语音识别模型并在精心设计的图片描述任务上使用，本研究构建了一个适用于普通话使用者的痴呆评估系统，通过提取语音识别模型的编码器并添加一个线性层用于痴呆评估，从 99 名被试中收集普通话语音数据并获取他们的临床评估，成功实现了 92.04% 的阿尔茨海默病检测准确率和 9% 的临床痴呆评分的平均绝对误差。