To BERT or Not To BERT: Comparing Speech and Language-based Approaches for Alzheimer's Disease Detection
This work addresses Alzheimer's detection for healthcare applications, but it is incremental as it compares existing methods on a specific dataset.
The paper tackled Alzheimer's disease detection by comparing hand-crafted linguistic/acoustic features with fine-tuned BERT models on the ADReSS dataset, finding that BERT outperformed feature-based approaches.
Research related to automatically detecting Alzheimer's disease (AD) is important, given the high prevalence of AD and the high cost of traditional methods. Since AD significantly affects the content and acoustics of spontaneous speech, natural language processing and machine learning provide promising techniques for reliably detecting AD. We compare and contrast the performance of two such approaches for AD detection on the recent ADReSS challenge dataset: 1) using domain knowledge-based hand-crafted features that capture linguistic and acoustic phenomena, and 2) fine-tuning Bidirectional Encoder Representations from Transformer (BERT)-based sequence classification models. We also compare multiple feature-based regression models for a neuropsychological score task in the challenge. We observe that fine-tuned BERT models, given the relative importance of linguistics in cognitive impairment detection, outperform feature-based approaches on the AD detection task.