Comparing Natural Language Processing Techniques for Alzheimer's Dementia Prediction in Spontaneous Speech

TL;DR

This study compares various NLP models, including traditional machine learning and Transformer-based approaches, for early Alzheimer's detection using spontaneous speech transcripts, achieving high classification accuracy.

Contribution

It systematically evaluates and compares multiple NLP techniques, highlighting the effectiveness of TF-IDF with SVM and DistilBERT embeddings for AD prediction.

Findings

TF-IDF + SVM achieved 0.81-0.82 classification accuracy

DistilBERT embeddings with linear models performed well

The models can predict AD and MMSE scores with promising results

Abstract

Alzheimer's Dementia (AD) is an incurable, debilitating, and progressive neurodegenerative condition that affects cognitive function. Early diagnosis is important as therapeutics can delay progression and give those diagnosed vital time. Developing models that analyse spontaneous speech could eventually provide an efficient diagnostic modality for earlier diagnosis of AD. The Alzheimer's Dementia Recognition through Spontaneous Speech task offers acoustically pre-processed and balanced datasets for the classification and prediction of AD and associated phenotypes through the modelling of spontaneous speech. We exclusively analyse the supplied textual transcripts of the spontaneous speech dataset, building and comparing performance across numerous models for the classification of AD vs controls and the prediction of Mental Mini State Exam scores. We rigorously train and evaluate Support Vector Machines (SVMs), Gradient Boosting Decision Trees (GBDT), and Conditional Random Fields (CRFs) alongside deep learning Transformer based models. We find our top performing models to be a simple Term Frequency-Inverse Document Frequency (TF-IDF) vectoriser as input into a SVM model and a pre-trained Transformer based model `DistilBERT' when used as an embedding layer into simple linear models. We demonstrate test set scores of 0.81-0.82 across classification metrics and a RMSE of 4.58.