A Survey on Automatic Multimodal Emotion Recognition in the Wild

2020 ◽  
pp. 35-64 ◽  
Author(s):  
Garima Sharma ◽  
Abhinav Dhall
Keyword(s):  
Emotion Recognition ◽  
In The Wild ◽  
Multimodal Emotion Recognition

scholarly journals HEU Emotion: a large-scale database for multimodal emotion recognition in the wild

2021 ◽  
Author(s):  
Jing Chen ◽  
Chenhui Wang ◽  
Kejun Wang ◽  
Chaoqun Yin ◽  
Cong Zhao ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Large Scale ◽  
In The Wild ◽  
Multimodal Emotion Recognition

Multimodal emotion recognition with hierarchical memory networks

2021 ◽  
Vol 25 (4) ◽  
pp. 1031-1045
Author(s):  
Helang Lai ◽  
Keke Wu ◽  
Lingli Li
Keyword(s):  
Emotion Recognition ◽  
The Self ◽  
Global Memory ◽  
Human Computer Interactions ◽  
Accuracy Improvement ◽  
Local Memory ◽  
Hierarchical Memory ◽  
Multimodal Emotion Recognition ◽  
Novel Model ◽  
Computer Interactions

Emotion recognition in conversations is crucial as there is an urgent need to improve the overall experience of human-computer interactions. A promising improvement in this field is to develop a model that can effectively extract adequate contexts of a test utterance. We introduce a novel model, termed hierarchical memory networks (HMN), to address the issues of recognizing utterance level emotions. HMN divides the contexts into different aspects and employs different step lengths to represent the weights of these aspects. To model the self dependencies, HMN takes independent local memory networks to model these aspects. Further, to capture the interpersonal dependencies, HMN employs global memory networks to integrate the local outputs into global storages. Such storages can generate contextual summaries and help to find the emotional dependent utterance that is most relevant to the test utterance. With an attention-based multi-hops scheme, these storages are then merged with the test utterance using an addition operation in the iterations. Experiments on the IEMOCAP dataset show our model outperforms the compared methods with accuracy improvement.

Multimodal Embeddings from Language Models for Emotion Recognition in the Wild

2021 ◽  
pp. 1-1
Author(s):  
Shao-Yen Tseng ◽  
Shrikanth Narayanan ◽  
Panayiotis Georgiou
Keyword(s):  
Emotion Recognition ◽  
Language Models ◽  
In The Wild

Speech Expression Multimodal Emotion Recognition Based on Deep Belief Network

2021 ◽  
Vol 19 (2) ◽  
Author(s):  
Dong Liu ◽  
Longxi Chen ◽  
Zhiyong Wang ◽  
Guangqiang Diao
Keyword(s):  
Emotion Recognition ◽  
Deep Belief Network ◽  
Belief Network ◽  
Multimodal Emotion Recognition

Hybrid Fusion Based Approach for Multimodal Emotion Recognition with Insufficient Labeled Data

2021 ◽  
Author(s):  
Puneet Kumar ◽  
Vedanti Khokher ◽  
Yukti Gupta ◽  
Balasubramanian Raman
Keyword(s):  
Emotion Recognition ◽  
Multimodal Emotion Recognition ◽  
Hybrid Fusion

Emotion Recognition Based on Body and Context Fusion in the Wild

2021 ◽  
Author(s):  
Yibo Huang ◽  
Hongqian Wen ◽  
Linbo Qing ◽  
Rulong Jin ◽  
Leiming Xiao
Keyword(s):  
Emotion Recognition ◽  
In The Wild

scholarly journals Construction of Spontaneous Emotion Corpus from Indonesian TV Talk Shows and Its Application on Multimodal Emotion Recognition

2018 ◽  
Vol E101.D (8) ◽  
pp. 2092-2100
Author(s):  
Nurul LUBIS ◽  
Dessi LESTARI ◽  
Sakriani SAKTI ◽  
Ayu PURWARIANTI ◽  
Satoshi NAKAMURA
Keyword(s):  
Emotion Recognition ◽  
Talk Shows ◽  
Multimodal Emotion Recognition
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