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Auditive Emotion Recognition for Empathic AI-Assistants

dc.contributor.authorDuwenbeck, Roswitha
dc.contributor.authorKirchner, Elsa Andrea
dc.date2024-11-01
dc.date.accessioned2025-01-13T11:15:17Z
dc.date.available2025-01-13T11:15:17Z
dc.date.issued2024
dc.description.abstractThis paper briefly introduces the Project “AudEeKA”, whose aim is to use speech and other bio signals for emotion recognition to improve remote, but also direct, healthcare. This article takes a look at use cases, goals and challenges, of researching and implementing a possible solution. To gain additional insights, the main-goal of the project is divided into multiple sub-goals, namely speech emotion recognition, stress detection and classification and emotion detection from physiological signals. Also, similar projects are considered and project-specific requirements stemming from use-cases introduced. Possible pitfalls and difficulties are outlined, which are mostly associated with datasets. They also emerge out of the requirements, their accompanying restrictions and first analyses in the area of speech emotion recognition, which are shortly presented and discussed. At the same time, first approaches to solutions for every sub-goal, which include the use of continual learning, and finally a draft of the planned architecture for the envisioned system, is presented. This draft presents a possible solution for combining all sub-goals, while reaching the main goal of a multimodal emotion recognition system.de
dc.identifier.doi10.1007/s13218-023-00828-3
dc.identifier.issn1610-1987
dc.identifier.urihttp://dx.doi.org/10.1007/s13218-023-00828-3
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/45581
dc.publisherSpringer
dc.relation.ispartofKI - Künstliche Intelligenz: Vol. 38, No. 3
dc.relation.ispartofseriesKI - Künstliche Intelligenz
dc.subjectContinual learning
dc.subjectEmotion recognition
dc.subjectMultimodal emotion recognition
dc.subjectSpeech emotion recognition
dc.titleAuditive Emotion Recognition for Empathic AI-Assistantsde
dc.typeText/Journal Article
mci.reference.pages151-156

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