Dementia Assessment Using Mandarin Speech with an Attention-based Speech Recognition Encoder

TL;DR

This study develops a Mandarin speech-based dementia assessment system using an attention-based speech recognition encoder, achieving high accuracy in detecting Alzheimer's and predicting dementia severity.

Contribution

It introduces a novel Mandarin-specific speech recognition model integrated with a dementia assessment module, enhancing detection accuracy over previous methods.

Findings

92.04% accuracy in Alzheimer's detection

9% mean absolute error in dementia score prediction

Effective use of attention-based encoder for clinical assessment

Abstract

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.