Analysis of Disfluencies for automatic detection of Mild Cognitive Impartment: a deep learning approach

TL;DR

This paper proposes a deep learning-based method using speech disfluency analysis to support early detection of Mild Cognitive Impairment, aiming to assist medical diagnosis with automated tools.

Contribution

It introduces a novel approach combining CNNs and feature selection techniques for automatic MCI detection from speech disfluencies.

Findings

Effective feature selection improves detection accuracy.

Deep learning models outperform traditional methods.

Potential for non-invasive early diagnosis of MCI.

Abstract

The so-called Mild Cognitive Impairment (MCI) or cognitive loss appears in a previous stage before Alzheimer's Disease (AD), but it does not seem sufficiently severe to interfere in independent abilities of daily life, so it usually does not receive an appropriate diagnosis. Its detection is a challenging issue to be addressed by medical specialists. This work presents a novel proposal based on automatic analysis of speech and disfluencies aimed at supporting MCI diagnosis. The approach includes deep learning by means of Convolutional Neural Networks (CNN) and non-linear multifeature modelling. Moreover, to select the most relevant features non-parametric Mann-Whitney U-testt and Support Vector Machine Attribute (SVM) evaluation are used.