Paladyn: A robot electronic device for multimodal emotional recognition of expressions

Nie, Lulu

A robot electronic device for multimodal emotional recognition of expressions / Nie, Lulu (CC BY)

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Full text : A robot electronic device for multimodal emotional recognition of expressions / Nie, Lulu (CC BY)

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CC BY: Attribution 4.0 International. You can find more information here.

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Title:: Paladyn

Publication:: Warsaw: De Gruyter

Note:: Gesehen am 03.01.22; Open Access; Namensnennung 4.0 International; Archivierung/Langzeitarchivierung gewährleistet

Scope:: Online-Ressource

ISSN:: 2081-4836

ZDB-ID:: 2550006-5

VÖBB-Katalog:: 35326634

Keywords:: Zeitschrift

Classification:: Informatik

Collection:: Informatik

Copyright:: Rights reserved

Accessibility:: Eingeschränkter Zugang mit Nutzungsbeschränkungen

Title:: Paladyn

Publication:: Warsaw: De Gruyter

Note:: Gesehen am 03.01.22; Open Access; Namensnennung 4.0 International; Archivierung/Langzeitarchivierung gewährleistet

Scope:: Online-Ressource

ISSN:: 2081-4836

ZDB-ID:: 2550006-5

VÖBB-Katalog:: 35326634

Keywords:: Zeitschrift

Classification:: Informatik

Collection:: Informatik

Copyright:: Rights reserved

Accessibility:: Eingeschränkter Zugang mit Nutzungsbeschränkungen

Article

Author:: Nie, Lulu

Title:: A robot electronic device for multimodal emotional recognition of expressions

Publication:: Warsaw: De Gruyter, 2024

Language:: English

Information:: Abstract: This study addresses the challenge of low recognition rates in emotion recognition systems, attributed to the vulnerability of sound data to ambient noise. To overcome this limitation, we propose a novel approach that leverages emotional information from diverse modalities. Our method integrates speech and facial expressions through advanced feature layer fusion and decision layer fusion strategies. Unlike traditional fusion algorithms, our proposed multimodal emotion recognition algorithm incorporates a dual fusion process at both the feature layer and the decision layer. This dual fusion not only preserves the distinctive characteristics of emotional information across modalities but also maintains inter-modal correlations. To evaluate the effectiveness of our approach, experiments were conducted using the eNTERFACE’05 multimodal emotion database. The results demonstrate a remarkable recognition accuracy of 89.3%, surpassing the highest recognition rate of 83.92% achieved by the current state-of-the-art kernel space feature fusion method. Our algorithm exhibits a significant improvement of 5.38% in recognition accuracy. By combining emotional data from speech and facial expressions using a data fusion methodology, our study demonstrates a significant improvement of 5.38% in recognition accuracy, contributing to the progress of multimodal emotion recognition systems.

Scope:: Online-Ressource

Note:: Open Access; Archivierung/Langzeitarchivierung gewährleistet

Keywords:: multimodal emotion recognition ; speech emotion recognition ; facial expression recognition ; convolutional recurrent neural network

Classification:: Informatik; Sonstiges

URN:: urn:nbn:de:101:1-2406241750028.728203009612

Collection:: Informatik; Sonstiges

Copyright:: CC BY

Accessibility:: Free Access

Author:: Nie, Lulu

Title:: A robot electronic device for multimodal emotional recognition of expressions

Publication:: Warsaw: De Gruyter, 2024

Language:: English

Information:: Abstract: This study addresses the challenge of low recognition rates in emotion recognition systems, attributed to the vulnerability of sound data to ambient noise. To overcome this limitation, we propose a novel approach that leverages emotional information from diverse modalities. Our method integrates speech and facial expressions through advanced feature layer fusion and decision layer fusion strategies. Unlike traditional fusion algorithms, our proposed multimodal emotion recognition algorithm incorporates a dual fusion process at both the feature layer and the decision layer. This dual fusion not only preserves the distinctive characteristics of emotional information across modalities but also maintains inter-modal correlations. To evaluate the effectiveness of our approach, experiments were conducted using the eNTERFACE’05 multimodal emotion database. The results demonstrate a remarkable recognition accuracy of 89.3%, surpassing the highest recognition rate of 83.92% achieved by the current state-of-the-art kernel space feature fusion method. Our algorithm exhibits a significant improvement of 5.38% in recognition accuracy. By combining emotional data from speech and facial expressions using a data fusion methodology, our study demonstrates a significant improvement of 5.38% in recognition accuracy, contributing to the progress of multimodal emotion recognition systems.

Scope:: Online-Ressource

Note:: Open Access; Archivierung/Langzeitarchivierung gewährleistet

Keywords:: multimodal emotion recognition ; speech emotion recognition ; facial expression recognition ; convolutional recurrent neural network

Classification:: Informatik; Sonstiges

URN:: urn:nbn:de:101:1-2406241750028.728203009612

Collection:: Informatik; Sonstiges

Copyright:: CC BY

Accessibility:: Free Access

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Full text : A robot electronic device for multimodal emotional recognition of expressions / Nie, Lulu (CC BY)

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Full text : A robot electronic device for multimodal emotional recognition of expressions / Nie, Lulu (CC BY) Access restriction There is no access restriction for this record. Copyright CC BY: Attribution 4.0 International. You can find more information here.

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Full text : A robot electronic device for multimodal emotional recognition of expressions / Nie, Lulu (CC BY)

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CC BY: Attribution 4.0 International. You can find more information here.