1. Co-regulation of the voice between patient and therapist in psychotherapy: machine learning for enhancing the synchronization of the experience of anger emotion : An experimental study proposal
- Author
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Silvia DellrOrco, Yuri Tedesco, Teresa Longobardi, Nelson Mauro Maldonato, Francesca Albano, Raffaele Sperandeo, Enrico Moretto, and Alfonso Davide Di Sarno
- Subjects
Facial expression ,Psychotherapist ,business.industry ,media_common.quotation_subject ,05 social sciences ,050109 social psychology ,Context (language use) ,Cognition ,Anger ,Machine learning ,computer.software_genre ,050105 experimental psychology ,Quality (philosophy) ,Natural (music) ,0501 psychology and cognitive sciences ,Artificial intelligence ,Psychology ,Affective computing ,business ,computer ,Gesture ,media_common - Abstract
This paper shows a high quality artificial cognitive capability, proposing an experimental study for the implementation of an intelligent affective computing monitoring system, based on a machine learning emotion classification.Affective computing is a research direction that aspires to bring the communication between humans and machines to a new level. This is possible by enabling machines to perceive and exhibit human-like qualities (e.g., facial expressions, bodily gestures and vocal qualities) during communication [1].The study proposal is applied in psychotherapy context for the recognition of the anger emotion by the voices of both the psychotherapist and the patient, in order to lead psychotherapists to an increasingly aware use of the voice.In a broader sense, the article belongs to the area of Cognitive infocommunications (CogInfoCom), which is an interdisciplinary research field that has emerged as a synergy between infocommunications and the cognitive sciences [2]. This merging and extension of cognitive capabilities is targeted towards engineering applications in which artificial and/or natural cognitive systems are enabled to work together more effectively.
- Published
- 2018
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