Contrast-enhanced Automatic Cognitive Impairment Detection System with Pause-encoder.

As the elderly population grows globally, health-care systems face a burden from the rise in Alzheimer’s patients due to an increase in demand for early diagnosis. Therefore, more people have started focusing on developing systems helping doctors diagnose Alzheimer’s, such as cognitive impairment detection systems. This paper presents a contrast-enhanced automatic cognitive impairment screening system combining paused-encoder based on the automatic transcription. We use the pre-trained automatic speech recognition model and adapt it to generate transcripts of the elderly’s speech. The pattern of pauses in speech is a commonly-studied acoustic feature since it can provide additional information besides the semantic information for the model prediction. The back-translation with contrastive learning is used to improve the encoded model further. The model also fine-tunes with the pause-encoded transcriptions to detect the cognitive impairment. Our result shows excellent performance with an accuracy of 81% in detecting Alzheimer’s disease. Also, the accuracy is acceptable on a more challenging task of detecting mild cognitive impairment, the middle stage between healthy and Alzheimer’s. In addition to the outperforming performance, our system is fully automatic and can be used easily.