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Dementia is a chronic neurological disease that causes cognitive disabilities and significantly impacts daily ac-tivities of affected individuals. It is known that early detection of dementia can improve the quality of life of patients through a specialized care program. Recently, there has been a growing interest in speech-based screening of neurological diseases such as dementia. The focus is on continuous monitoring of changes in speech of dementia patients, aiming to identify the early onset of the disease which could facilitate development of preventative treatment care. In this work, we propose a dynamic (temporal) modeling of acoustic speech characteristics aiming at identifying the signs of dementia. The classification performance of the proposed framework is compared with a baseline static modeling of acoustic speech features. Experimental results show that the proposed dynamic approach outperforms the static method. It achieves the classification accuracy of 74.55% compared to 66.92% obtained using the static models.

Static vs. Dynamic Modelling of Acoustic Speech Features for Detection of Dementia