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The Resource Multi-modal emotion detection using deep learning for interpersonal communication analytics, Sravanthi Gogadi

Multi-modal emotion detection using deep learning for interpersonal communication analytics, Sravanthi Gogadi

Creator
Author
Contributor
Summary
In recent years, deep learning technologies have been increasingly applied to generate meaningful data for advanced research in humanities and sciences. Interpersonal communication skills are crucial to success in science. Communication skills, either in a small group learning environment or a large group setting, are always useful in any future workplace. In this study, we aim to analyze mutual communication and interactions between speakers/audiences from a broader perspective, including emotional and cognitive interactions, in TED talk or classroom settings. We are mainly interested in the recognition of facial and gesture emotions captured in such contexts. More specifically, we proposed a multi-modal emotion detection approach for facial expression, e.g., facial sentiment, gender, age, ethnicity, hairstyles, as well as gesture expression, e.g., sitting, standing, raising their hands, folded hands and crossed legs. The real-time feedback of the proposed system on individual or group communication can effectively be used for improving their communication skills
Language
eng
Work
Publication
Extent
1 online resource (44 pages)
Note
  • "A thesis in Computer Science."
  • Advisor: Yugyung Lee
  • Vita
Contents
  • Introduction
  • Background and related work
  • Proposed framework
  • Conclusion and future work
Instance
Label
Multi-modal emotion detection using deep learning for interpersonal communication analytics
Title
Multi-modal emotion detection using deep learning for interpersonal communication analytics
Statement of responsibility
Sravanthi Gogadi
Creator
Contributor
Author
Degree supervisor
Subject
Genre
Language
eng
Summary
In recent years, deep learning technologies have been increasingly applied to generate meaningful data for advanced research in humanities and sciences. Interpersonal communication skills are crucial to success in science. Communication skills, either in a small group learning environment or a large group setting, are always useful in any future workplace. In this study, we aim to analyze mutual communication and interactions between speakers/audiences from a broader perspective, including emotional and cognitive interactions, in TED talk or classroom settings. We are mainly interested in the recognition of facial and gesture emotions captured in such contexts. More specifically, we proposed a multi-modal emotion detection approach for facial expression, e.g., facial sentiment, gender, age, ethnicity, hairstyles, as well as gesture expression, e.g., sitting, standing, raising their hands, folded hands and crossed legs. The real-time feedback of the proposed system on individual or group communication can effectively be used for improving their communication skills
Cataloging source
UMK
http://library.link/vocab/creatorName
Gogadi, Sravanthi
Degree
M.S.
Dissertation note
(School of Computing and Engineering).
Dissertation year
2019.
Granting institution
University of Missouri-Kansas City,
Illustrations
illustrations
Index
no index present
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
  • theses
http://library.link/vocab/relatedWorkOrContributorDate
1960-
http://library.link/vocab/relatedWorkOrContributorName
Lee, Yugyung
http://library.link/vocab/subjectName
  • Emotion recognition
  • Machine learning
Label
Multi-modal emotion detection using deep learning for interpersonal communication analytics, Sravanthi Gogadi
Instantiates
Publication
Copyright
Note
  • "A thesis in Computer Science."
  • Advisor: Yugyung Lee
  • Vita
Antecedent source
not applicable
Bibliography note
Includes bibliographical references (pages 41-43
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
black and white
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
Introduction -- Background and related work -- Proposed framework -- Conclusion and future work
Control code
1137603992
Dimensions
unknown
Extent
1 online resource (44 pages)
File format
one file format
Form of item
online
Level of compression
mixed
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other physical details
illustrations.
Quality assurance targets
not applicable
Specific material designation
remote
System control number
(OCoLC)1137603992
System details
  • The full text of the thesis is available as an Adobe Acrobat .pdf file; Adobe Acrobat Reader required to view the file
  • Mode of access: World Wide Web
Label
Multi-modal emotion detection using deep learning for interpersonal communication analytics, Sravanthi Gogadi
Publication
Copyright
Note
  • "A thesis in Computer Science."
  • Advisor: Yugyung Lee
  • Vita
Antecedent source
not applicable
Bibliography note
Includes bibliographical references (pages 41-43
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
black and white
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
Introduction -- Background and related work -- Proposed framework -- Conclusion and future work
Control code
1137603992
Dimensions
unknown
Extent
1 online resource (44 pages)
File format
one file format
Form of item
online
Level of compression
mixed
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other physical details
illustrations.
Quality assurance targets
not applicable
Specific material designation
remote
System control number
(OCoLC)1137603992
System details
  • The full text of the thesis is available as an Adobe Acrobat .pdf file; Adobe Acrobat Reader required to view the file
  • Mode of access: World Wide Web

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