Speech‐Based Detection of Alzheimer's Disease: Leveraging Spectral Contrast and Pitch Variability as Potential Diagnostic Markers

TL;DR

This study explores how speech features like pitch and clarity can help detect Alzheimer's disease and assess cognitive decline.

Contribution

The study identifies spectral contrast and pitch variability as novel acoustic markers for Alzheimer's detection.

Findings

AD participants showed significantly lower spectral contrast, pitch mean, and pitch standard deviation compared to healthy controls.

All three acoustic features correlated significantly with MMSE scores, indicating their potential for cognitive assessment.

Effect sizes were medium, suggesting these features could be useful in early Alzheimer's detection.

Abstract

Alzheimer's disease (AD) profoundly affects motor control and cognitive functions, often resulting in impaired speech characteristics such as vocal clarity, emotional expressiveness, and prosodic richness. To detect such abnormalities, we examine the role of three specific acoustic features to differentiate participants with AD from healthy controls (HC) and study the association between these acoustic features and global cognition.

Speech data from 237 participants (115 HC, 110 AD) in the ADReSS‐M dataset, collected during the “Cookie Theft” picture description task, were analyzed. This dataset has been matched for age and gender by propensity score to prevent bias. The HC group averaged 66.4 years (SD: 6.64), and the AD group averaged 69.4 years (SD: 6.92), with significantly lower Mini‐Mental State Examination (MMSE) scores in AD (AD: 17.9; HC: 29.0). Features quantifying vocal clarity, articulatory precision (spectral contrast), vocal tone (pitch mean), and prosodic variability (pitch standard deviation) were extracted. Group differences in these features were assessed using t‐tests, and Pearson correlation analyses were conducted to examine associations between acoustic measures and MMSE scores.

There were significant differences between AD and HC groups for spectral contrast (t(235) = 4.26, p <0.0001), pitch mean (t(235) = 3.54, p = 0.0005), and pitch standard deviation (t(235) = 3.62, p = 0.0004). Cohen's d values for these features ranged from ‐0.5 to ‐0.6, indicating medium effect sizes, with lower values observed in the AD group. We also found a significant correlation (p <0.01) between MMSE scores and each of the features (Pearson's r = 0.22 for pitch mean, 0.23 for pitch standard deviation, and 0.25 for spectral contrast).

This preliminary study highlights the physiological basis of altered speech patterns in AD and their diagnostic relevance. Future work will focus on refining preprocessing algorithms and incorporating advanced feature extraction methods to enhance the effect sizes and correlation for AD detection and cognitive assessment.