Exploring the efficacy of text embeddings in early dementia diagnosis from speech
Résumé
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 discrimina-
tive 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
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