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Dementia is a severe cognitive impairment that affects the health of older adults and creates a burden on their families and caretakers. This paper analyzes diverse hand-crafted features extracted from spoken languages and selects the most discriminative ones for dementia detection. Recently, the performance of dementia detection has been significantly improved by utilizing Transformer-based models that automatically capture the structural and linguistic properties of spoken languages. We investigate Transformer-based features and propose an end-to-end system for dementia detection.