Advanced speech biomarker integration for robust Alzheimer’s disease diagnosis

Abstract

The healthcare sector has witnessed a transformative shift in recent years, driven by rapid advancements in digital technologies. Among the myriad of applications, the management of Alzheimer’s disease (AD) has garnered significant attention. AD, the most common form of dementia, affects millions globally and presents a significant challenge due to its progressive and currently incurable nature. Early detection is crucial, yet existing diagnostic methods are invasive, expensive, and not readily accessible. This study proposes a hybrid approach combining traditional acoustic features (e.g., MFCC, pitch, jitter, shimmer) with deep learning-based embeddings (YAMNet, VGGish) to enhance the robustness and accuracy of AD detection through speech analysis. The methodology involves comprehensive feature extraction, dimensionality reduction via autoencoders, and classification using advanced machine learning (ML) and deep learning (DL) models. Evaluation on the ADReSS dataset demonstrates the proposed method’s superior performance, achieving an accuracy of 89.9% with a deep neural network classifier. The results highlight the potential of integrating traditional and modern techniques to develop non-invasive, cost-effective, and accessible tools for early AD detection, paving the way for timely intervention and improved patient outcomes. Future work will focus on expanding datasets, incorporating diverse demographics, and refining models for better sensitivity and specificity in clinical applications.