This study scrutinizes the impacts of utilizing a socially assistive robot, the RASA robot, during speech therapy sessions for children with language disorders. Two capabilities were developed for the robotic platform to enhance children-robot interactions during speech therapy interventions: facial expression communication (containing recognition and expression) and lip-syncing. Facial expression recognition was conducted by training several well-known CNN architectures on one of the most extensive facial expressions databases, the AffectNet database, and then modifying them using the transfer learning strategy performed on the CK+ dataset.
View Article and Find Full Text PDFLack of educational facilities for the burgeoning world population, financial barriers, and the growing tendency in favor of inclusive education have all helped channel a general inclination toward using various educational assistive technologies, e.g., socially assistive robots.
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