Transformer-Based Deep Learning Approaches for Speech-Based Dementia Detection: A Systematic Review.

As the population of older adults continues growing, so will the need for cost-effective approaches to early dementia detection. Deep learning approaches using patient speech samples show promising results. This systematic review examines studies utilizing speech-based deep learning for dementia diagnosis with the objective of identifying best practices for future data-driven dementia research. Studies researching speech-based deep learning for dementia were obtained from PubMed, Wiley Library, Science Direct, IEEE, Web of Science, Google Scholar, and arXiv. 80 studies were reviewed. Studies were analyzed in terms of model architecture and performance, speech features employed, and databases used. We observed that transformer-based approaches were most frequent, achieving an average accuracy of 85.71%, and that linguistic features outperform acoustic features. Our review identified several limitations, including a lack of dataset diversity, inconsistent classification of dementia severity levels across studies, and variability in how sample sizes and model performance metrics (e.g., accuracy, sensitivity, specificity) are reported. These inconsistencies hinder direct comparisons between studies and limit the reproducibility of findings. Still, our findings suggest that incorporation of transformers into current speech-based deep learning models can further improve detection of cognitive impairment. Consideration of our observations in future data-driven dementia research will lead to advancements in the development of diagnostic decision support systems for clinical practice.