ISCA Archive Interspeech 2021
ISCA Archive Interspeech 2021

Automatic Detection of Alzheimer’s Disease Using Spontaneous Speech Only

Jun Chen, Jieping Ye, Fengyi Tang, Jiayu Zhou

Alzheimer’s disease (AD) is a neurodegenerative syndrome which affects tens of millions of elders worldwide. Although there is no treatment currently available, early recognition can improve the lives of people with AD and their caretakers and families. To find a cost-effective and easy-to-use method for dementia detection and address the dementia classification task of InterSpeech 2021 ADReSSo (Alzheimer’s Dementia Recognition through Spontaneous Speech only) challenge, we conduct a systematic comparison of approaches to detection of cognitive impairment based on spontaneous speech. We investigated the characteristics of acoustic modality and linguistic modality directly based on the audio recordings of narrative speech, and explored a variety of modality fusion strategies. With an ensemble over top-10 classifiers on the training set, we achieved an accuracy of 81.69% compared to the baseline of 78.87% on the test set. The results suggest that although transcription errors will be introduced through automatic speech recognition, integrating textual information generally improves classification performance. Besides, ensemble methods can boost both the accuracy and the robustness of models.

doi: 10.21437/Interspeech.2021-2002

Cite as: Chen, J., Ye, J., Tang, F., Zhou, J. (2021) Automatic Detection of Alzheimer’s Disease Using Spontaneous Speech Only. Proc. Interspeech 2021, 3830-3834, doi: 10.21437/Interspeech.2021-2002

  author={Jun Chen and Jieping Ye and Fengyi Tang and Jiayu Zhou},
  title={{Automatic Detection of Alzheimer’s Disease Using Spontaneous Speech Only}},
  booktitle={Proc. Interspeech 2021},