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 disease (AD).With the rapid growth of Large Language Models, natural language processing (NLP) has become 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.