A classification study of speech productions produced by healthy speakers and speakers with Mild Cognitive Impairment using Deep Sequential Neural Networks

@inProceedings{Themistocleous-Charalambos2018-265821, title = {A classification study of speech productions produced by healthy speakers and speakers with Mild Cognitive Impairment using Deep Sequential Neural Networks}, abstract = {Mild cognitive impairment (MCI) is a neurological condition, which is characterized by a noticeable decline of cognitive abilities, including communicative and linguistic skills. Nevertheless, evidence from speech production has been inconsistent with respect to features and factors that are most affected. This study employs speech properties from vowels, produced in a reading task by 55 Swedish speakers—30 healthy controls and 25 MCI — and aims to distinguish MCI and healthy productions. The study presents two machine learning classification tasks. The first is a classification of speech productions as MCI or healthy and the second is a classification of speakers as MCI or healthy. The study evaluates several Deep Neural Network Architectures that resulted in high classification accuracy of MCI and healthy speech productions and MCI and healthy speakers. The proposed neural models can be employed in methods of early detection of cognitive decline in order to quantify the progression of the disease and to provide suitable therapeutics. }, booktitle = {The Sixth IEEE International Conference on Healthcare Informatics (IEEE-ICHI 2018), New York, USA.}, author = {Themistocleous, Charalambos and Eckerström, Marie and Kokkinakis, Dimitrios}, year = {2018}, }