A Content and Knowledge Management System Supporting Emotion Detection from Speech

Binh Vu; Mikel  deVelasco; PM McKevitt; RR Bond; Robin Turkington; Frederick Booth; Maurice Mulvenna; Michael Fuchs; Matthias Hemmje

doi:10.1007/978-981-15-8395-7_28

A Content and Knowledge Management System Supporting Emotion Detection from Speech

Binh Vu, Mikel deVelasco, PM McKevitt, RR Bond, Robin Turkington, Frederick Booth, Maurice Mulvenna, Michael Fuchs, Matthias Hemmje

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Conversational Dialogue Systems for the Next Decade, IWSDS 2020
Subtitle of host publication	IWSDS 2020
Editors	Luis Fernando D’Haro, Zoraida Callejas, Satoshi Nakamura
Place of Publication	Madrid, Spain
Publisher	Springer Cham
Pages	369-378
Number of pages	10
ISBN (Electronic)	978-981-15-8395-7
ISBN (Print)	978-981-15-8394-0
DOIs	https://doi.org/10.1007/978-981-15-8395-7_28
Publication status	Published (in print/issue) - 25 Oct 2020

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	704

Bibliographical note

Funding Information:
Acknowledgements This publication has been produced in the context of the MENHIR project. This project has received funding from the European Union’s H2020 Programme under grant agreement No. 823907. However, this paper reflects only the authors’ view and the European Commission is not responsible for any use that may be made of the information it contains.

Publisher Copyright:
© 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-981-15-8395-7_28

Cite this

Vu, B., deVelasco, M., McKevitt, PM., Bond, RR., Turkington, R., Booth, F., Mulvenna, M., Fuchs, M., & Hemmje, M. (2020). A Content and Knowledge Management System Supporting Emotion Detection from Speech. In L. F. D’Haro, Z. Callejas, & S. Nakamura (Eds.), Conversational Dialogue Systems for the Next Decade, IWSDS 2020: IWSDS 2020 (pp. 369-378). (Lecture Notes in Electrical Engineering ; Vol. 704). Springer Cham. https://doi.org/10.1007/978-981-15-8395-7_28

Vu, Binh ; deVelasco, Mikel ; McKevitt, PM et al. / A Content and Knowledge Management System Supporting Emotion Detection from Speech. Conversational Dialogue Systems for the Next Decade, IWSDS 2020: IWSDS 2020 . editor / Luis Fernando D’Haro ; Zoraida Callejas ; Satoshi Nakamura. Madrid, Spain : Springer Cham, 2020. pp. 369-378 (Lecture Notes in Electrical Engineering ).

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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{\textquoteright}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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Vu, B, deVelasco, M, McKevitt, PM, Bond, RR, Turkington, R, Booth, F, Mulvenna, M, Fuchs, M & Hemmje, M 2020, A Content and Knowledge Management System Supporting Emotion Detection from Speech. in LF D’Haro, Z Callejas & S Nakamura (eds), Conversational Dialogue Systems for the Next Decade, IWSDS 2020: IWSDS 2020 . Lecture Notes in Electrical Engineering , vol. 704, Springer Cham, Madrid, Spain, pp. 369-378. https://doi.org/10.1007/978-981-15-8395-7_28

A Content and Knowledge Management System Supporting Emotion Detection from Speech. / Vu, Binh; deVelasco, Mikel ; McKevitt, PM et al.
Conversational Dialogue Systems for the Next Decade, IWSDS 2020: IWSDS 2020 . ed. / Luis Fernando D’Haro; Zoraida Callejas; Satoshi Nakamura. Madrid, Spain: Springer Cham, 2020. p. 369-378 (Lecture Notes in Electrical Engineering ; Vol. 704).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Vu B, deVelasco M, McKevitt PM, Bond RR, Turkington R, Booth F et al. A Content and Knowledge Management System Supporting Emotion Detection from Speech. In D’Haro LF, Callejas Z, Nakamura S, editors, Conversational Dialogue Systems for the Next Decade, IWSDS 2020: IWSDS 2020 . Madrid, Spain: Springer Cham. 2020. p. 369-378. (Lecture Notes in Electrical Engineering ). doi: 10.1007/978-981-15-8395-7_28

A Content and Knowledge Management System Supporting Emotion Detection from Speech

Abstract

Publication series

Bibliographical note

UN SDGs

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