Using Corrected ASR Projection to Improve AD Recognition Performance from Spontaneous Speech

Abstract

Alzheimer's Disease patients often exhibit cognitive decline, with language impairment being a prominent biomarker. Spontaneous speech analysis provides a non-invasive screening approach for AD. Large language models, increasingly employed for textual feature extraction, show potential in early AD prediction. However, Automatic Speech Recognition transcription errors, stemming from language impairments in AD and Mild Cognitive Impairment patients, can lead to information loss during feature extraction. To mitigate this, we introduce the Corrected ASR Projecting, CAP model. During training, ASR-transcribed text is manually corrected one by one, and then textual features are extracted independently using BERT, Claude, GLM, and GPT-3. The CAP model is trained by aligning the ASR transcription feature space with the corrected ASR transcription feature space. Experiments on the NCMMSC 2021 dataset demonstrate that the CAP model improves classification performance for AD recognition, with the maximum accuracy improvement reaching 5.55%.