Comparing Acoustic-based Approaches for Alzheimer's Disease Detection
This work addresses Alzheimer's disease detection for healthcare applications, but it is incremental as it compares existing methods on a specific dataset.
The paper tackled Alzheimer's disease detection from speech by comparing acoustic features and pre-trained embeddings on the ADReSSo dataset, finding that embedding-based approaches achieved higher and more balanced performance, with the best model outperforming the baseline by 2.8%.
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%.