What is it about?
This study investigates the speech articulatory coordination in schizophrenia subjects exhibiting strong positive symptoms (e.g. hallucinations and delusions), using two distinct channel-delay correlation methods. We show that the schizophrenic subjects with strong positive symptoms and who are markedly ill pose complex articulatory coordination pattern in facial and speech gestures than what is observed in healthy subjects.
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Why is it important?
This paper proposes a multimodal approach to classify subjects with strong positive symptoms in schizophrenia from healthy. We also show that the video based features are more effective in identifying articulatory coordination changes while also asserting the fact that fusing with audio based TVs significantly boost the performance in classification
Perspectives
This has been a very rewarding research with collaborations from experts from multi-disciplinary backgrounds. We continue to work on this project so that we can develop a real world application for screening people with strong positive symptoms in schizophrenia. I hope everyone will stay tuned for the future developments of this project.
Yashish M Siriwardena
University of Maryland at College Park
Read the Original
This page is a summary of: Multimodal Approach for Assessing Neuromotor Coordination in Schizophrenia Using Convolutional Neural Networks, October 2021, ACM (Association for Computing Machinery),
DOI: 10.1145/3462244.3479967.
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