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M3ER: Multiplicative Multimodal Emotion Recognition Using Facial, Textual, and Speech Cues [article]

Trisha Mittal, Uttaran Bhattacharya, Rohan Chandra, Aniket Bera, Dinesh Manocha
2019 arXiv   pre-print
We present M3ER, a learning-based method for emotion recognition from multiple input modalities.  ...  Our approach combines cues from multiple co-occurring modalities (such as face, text, and speech) and also is more robust than other methods to sensor noise in any of the individual modalities.  ...  We would also like to thank Abhishek Bassan and Ishita Verma for initial few discussions on this project.  ... 
arXiv:1911.05659v2 fatcat:vthrawfkmjeohcy3nzzgsegdcu

M3ER: Multiplicative Multimodal Emotion Recognition using Facial, Textual, and Speech Cues

Trisha Mittal, Uttaran Bhattacharya, Rohan Chandra, Aniket Bera, Dinesh Manocha
2020 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
We present M3ER, a learning-based method for emotion recognition from multiple input modalities.  ...  Our approach combines cues from multiple co-occurring modalities (such as face, text, and speech) and also is more robust than other methods to sensor noise in any of the individual modalities.  ...  We would also like to thank Abhishek Bassan and Ishita Verma for initial few discussions on this project.  ... 
doi:10.1609/aaai.v34i02.5492 fatcat:pru3c47jf5b45nixfnqx7eikyu

A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances [article]

Yan Wang, Wei Song, Wei Tao, Antonio Liotta, Dawei Yang, Xinlei Li, Shuyong Gao, Yixuan Sun, Weifeng Ge, Wei Zhang, Wenqiang Zhang
2022 arXiv   pre-print
., emotion recognition and sentiment analysis).  ...  Physical-based affect recognition caters to more researchers due to multiple public databases.  ...  [351] (Fig. 6 (c )) investigated a multiplicative multimodal emotion (M3ER) recognition in three steps: firstly, feature vectors are extracted from the raw three modalities; then, these features are  ... 
arXiv:2203.06935v3 fatcat:h4t3omkzjvcejn2kpvxns7n2qe

Adaptive Multimodal Emotion Detection Architecture for Social Robots

Juanpablo Heredia, Edmundo Lopes-Silva, Yudith Cardinale, Jose Diaz-Amado, Irvin Dongo, Wilfredo Graterol, Ana Aguilera
2022 IEEE Access  
Emotion recognition is a strategy for social robots used to implement better Human-Robot Interaction and model their social behaviour.  ...  Since human emotions can be expressed in different ways (e.g., face, gesture, voice), multimodal approaches are useful to support the recognition process.  ...  A simple CNN network from facial emotion recognition and CNN network with log-Mel spectrogram as input from speech emotion recognition are used for the combined emotion recognition model.  ... 
doi:10.1109/access.2022.3149214 fatcat:ehlptl44yneufdthidrkgjecvm

Transfer Learning in Conversational Analysis through Reusing Preprocessing Data as Supervisors [article]

Joshua Yee Kim, Tongliang Liu, Kalina Yacef
2021 arXiv   pre-print
Extensive experiments on IEMOCAP and SEMAINE data validate the improvements over single-task approaches, and suggest that it may generalize across multiple primary tasks.  ...  Conversational analysis systems are trained using noisy human labels and often require heavy preprocessing during multi-modal feature extraction.  ...  In Pro- Multiplicative multimodal emotion recognition us- ceedings of the 29th ACM International Conference ing facial, textual, and speech cues.  ... 
arXiv:2112.03032v1 fatcat:rxcey4hz2nacxoka4fmhhfckxq

Tailor Versatile Multi-modal Learning for Multi-label Emotion Recognition [article]

Yi Zhang, Mingyuan Chen, Jundong Shen, Chongjun Wang
2022 arXiv   pre-print
Multi-modal Multi-label Emotion Recognition (MMER) aims to identify various human emotions from heterogeneous visual, audio and text modalities.  ...  In this paper, we propose versaTile multi-modAl learning for multI-labeL emOtion Recognition (TAILOR), aiming to refine multi-modal representations and enhance discriminative capacity of each label.  ...  tion recognition using facial, textual, and speech cues. In Goodfellow, I.; Pouget-Abadie, J.; Mirza, M.; Xu, B.; AAAI, volume 34, 1359–1367.  ... 
arXiv:2201.05834v1 fatcat:ua2xxwh7ezcuvd2o2sbexymbmi