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

Zih-Jyun Lin, Yi-Ju Chen, Po-Chih Kuo, Likai Huang, Chaur-Jong Hu +1 more
10/6/2023
cs.CLcs.LGcs.SD

No official code found. Best implementation: jason7580/End-to-End-ASR-and-Dementia-detection-system (6 stars, last commit 2024-09-27).

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.

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Code Implementations(1)

jason7580/End-to-End-ASR-and-Dementia-detection-systemCommunity100%
61Python, Jupyter NotebookJul 26, 20232 years ago

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Cite this paper

@article{lin2023dementia,
  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},
  year   = {2023},
  eprint = {2310.03985},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2310.03985v2}
}

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