Exploring the Efficacy of Text Embeddings in Early Dementia Diagnosis from Speech

Language impairment is a key biomarker for neurodegenerative diseases such as Alzheimer's dis-ease (AD). With the rapid growth of Large Language Models, natural language processing (NLP) has be-come a preferred modality for the early prediction of AD from speech. In this work, we propose a two-stage process for early detection of AD from transcriptions of speech. The first step involves extracting a discriminative text embedding representation using public models from OpenAI. This embedding serves as input for a machine learning classifier in the second stage. In this paper, we investigate three text embedding models and eight machine learning classifiers, both deep learning (DL) based and non-DL based. The evaluation was conducted using the public ADReSSo dataset of 237 patients. The results show that models “ada-002” and “3-small” produce discriminative embeddings that lead to good performance when combined with a Deep Neural Network in classification, achieving accuracy rates of 83.10% and 84.51 %, respectively.