The Development of Interactive Conversational Agent Software for Dementia Patient

Abstract

In this study, we developed an interactive conversational agent software to ameliorate the sympthon of dimentia patients. This software works as a speech therapy tool, which acts as a conversation partner to a patient. We defined three sets of reminiscent questions into the software. Each set containes 15 questions. The software utilizes constrained local model (CLM) and voice detections to determine the utterances of patients. Once the CLM recognizes a patient's facial landmarks, it starts to ask him using the pre-defined questions. The software will continue to ask using subsequent questions when it doesn't detect utterances from either distance changes between mouth landmarks or changes of voice of the patient. Our experiments show that the voice detection solely enables utterance detections in a low environmental noise while the CLM succeeds to detect utterances regardless of the environmental noise.