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.

翻译：痴呆症诊断需要一系列不同的测试方法，过程复杂且耗时。早期检测痴呆症至关重要，因为这可以防止病情进一步恶化。本文利用语音识别模型，构建了一套针对普通话使用者在图片描述任务中的痴呆评估系统。通过在接近真实场景的语音数据上训练基于注意力机制的语音识别模型，我们显著提升了模型的识别能力。随后，我们从语音识别模型中提取编码器，并添加线性层进行痴呆评估。我们收集了99名受试者的普通话语音数据，并从当地医院获取了其临床评估结果。在阿尔茨海默病检测中，我们实现了92.04%的准确率，在临床痴呆评定量表得分预测中，平均绝对误差为9%。