Dementia classification from spontaneous speech using wrapper-based feature selection
Dementia encompasses a group of syndromes that impair cognitive functions such as memory, reasoning, and the ability to perform daily activities. As populations globally age, nearly 10 million new dementia cases occur annually. Clinical diagnosis remains challenging because symptoms overlap with other conditions and require comprehensive cognitive assessment, highlighting the need for feasible and accurate detection methods. Recent advances in machine learning have highlighted spontaneous speech as a promising noninvasive, cost-effective, and scalable biomarker for dementia detection. In this study, spontaneous speech recordings from the ADReSS dataset and the extended Pitt Corpus were analyzed, consisting of picture description tasks performed by cognitively healthy individuals and participants with Alzheimer's disease or dementia. Unlike many prior approaches relying on speech-active segments, acoustic features were extracted from entire recordings with the openSMILE toolkit. This recording-level representation reduces the number of feature vectors and provides a computationally efficient framework for dementia classification, while indirectly incorporating pause- and hesitation-related information. Classification models with classifier-based wrapper feature selection were employed to estimate feature importance and identify diagnostically relevant acoustic characteristics. Among the evaluated classifiers, the extreme minimal learning machine emerged as the most computationally efficient method, providing competitive classification accuracy with substantially lower training time in repeated leave-one-subject-out validation. The results demonstrated that the proposed framework is computationally efficient, interpretable, and well-suited as a supportive tool for speech-based dementia assessment.