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End-to-end multimodal emotion recognition using deep neural networks

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Title: End-to-end multimodal emotion recognition using deep neural networks
Authors: Tzirakis
Trigeorgis
Nicolaou
Schuller
Zafeiriou, S
Item Type: Journal Article
Abstract: Automatic affect recognition is a challenging task due to the various modalities emotions can be expressed with. Applications can be found in many domains including multimedia retrieval and human-computer interaction. In recent years, deep neural networks have been used with great success in determining emotional states. Inspired by this success, we propose an emotion recognition system using auditory and visual modalities. To capture the emotional content for various styles of speaking, robust features need to be extracted. To this purpose, we utilize a convolutional neural network (CNN) to extract features from the speech, while for the visual modality a deep residual network of 50 layers is used. In addition to the importance of feature extraction, a machine learning algorithm needs also to be insensitive to outliers while being able to model the context. To tackle this problem, long short-term memory networks are utilized. The system is then trained in an end-to-end fashion where-by also taking advantage of the correlations of each of the streams-we manage to significantly outperform, in terms of concordance correlation coefficient, traditional approaches based on auditory and visual handcrafted features for the prediction of spontaneous and natural emotions on the RECOLA database of the AVEC 2016 research challenge on emotion recognition.
Issue Date: 1-Dec-2017
Date of Acceptance: 16-Oct-2017
URI: http://hdl.handle.net/10044/1/52827
DOI: https://dx.doi.org/10.1109/JSTSP.2017.2764438
ISSN: 1932-4553
Publisher: Institute of Electrical and Electronics Engineers
Start Page: 1301
End Page: 1309
Journal / Book Title: IEEE Journal of Selected Topics in Signal Processing
Volume: 11
Issue: 8
Copyright Statement: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Keywords: 0906 Electrical And Electronic Engineering
Networking & Telecommunications
Publication Status: Published
Online Publication Date: 2017-10-18
Appears in Collections:Faculty of Engineering
Computing



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