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Conference Papers Year : 2024

Exploring the efficacy of text embeddings in early dementia diagnosis from speech

Mohamed Ibn Khedher
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Olfa Jemai
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Abstract

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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Dates and versions

hal-04596848 , version 1 (31-05-2024)

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  • HAL Id : hal-04596848 , version 1

Cite

Khaoula Ajroudi, Mohamed Ibn Khedher, Olfa Jemai, Mounim A El-Yacoubi. Exploring the efficacy of text embeddings in early dementia diagnosis from speech. International Conference on Human System Interaction, Jul 2024, Paris, France. ⟨hal-04596848⟩
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