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2021 | OriginalPaper | Chapter

A Content and Knowledge Management System Supporting Emotion Detection from Speech

Authors : Binh Vu, Mikel deVelasco, Paul Mc Kevitt, Raymond Bond, Robin Turkington, Frederick Booth, Maurice Mulvenna, Michael Fuchs, Matthias Hemmje

Published in: Conversational Dialogue Systems for the Next Decade

Publisher: Springer Singapore

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Abstract

Emotion recognition has recently attracted much attention in both industrial and academic research as it can be applied in many areas from education to national security. In healthcare, emotion detection has a key role as emotional state is an indicator of depression and mental disease. Much research in this area focuses on extracting emotion related features from images of the human face. Nevertheless, there are many other sources that can identify a person’s emotion. In the context of MENHIR, an EU-funded R&D project that applies Affective Computing to support people in their mental health, a new emotion-recognition system based on speech is being developed. However, this system requires comprehensive data-management support in order to manage its input data and analysis results. As a result, a cloud-based, high-performance, scalable, and accessible ecosystem for supporting speech-based emotion detection is currently developed and discussed here.

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Metadata
Title
A Content and Knowledge Management System Supporting Emotion Detection from Speech
Authors
Binh Vu
Mikel deVelasco
Paul Mc Kevitt
Raymond Bond
Robin Turkington
Frederick Booth
Maurice Mulvenna
Michael Fuchs
Matthias Hemmje
Copyright Year
2021
Publisher
Springer Singapore
DOI
https://doi.org/10.1007/978-981-15-8395-7_28