Comparing Acoustic-based Approaches for Alzheimer's Disease Detection

TL;DR

This study compares three acoustic-based methods for Alzheimer's detection from speech, highlighting the superior generalizability and balanced performance of embedding-based approaches over traditional features.

Contribution

It evaluates and compares conventional acoustic features, pre-trained acoustic embeddings, and their combination for AD detection, demonstrating the advantages of embedding-based methods.

Findings

Embedding-only approaches are more generalizable.

Pre-trained embeddings yield more balanced performance.

Best model outperforms baseline by 2.8%.

Abstract

Robust strategies for Alzheimer's disease (AD) detection are important, given the high prevalence of AD. In this paper, we study the performance and generalizability of three approaches for AD detection from speech on the recent ADReSSo challenge dataset: 1) using conventional acoustic features 2) using novel pre-trained acoustic embeddings 3) combining acoustic features and embeddings. We find that while feature-based approaches have a higher precision, classification approaches relying on pre-trained embeddings prove to have a higher, and more balanced cross-validated performance across multiple metrics of performance. Further, embedding-only approaches are more generalizable. Our best model outperforms the acoustic baseline in the challenge by 2.8%.