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Audiovisual emotion recognition in wild

Egils Avots, Tomasz Sapiński, Maie Bachmann, Dorota Kamińska
2018 Machine Vision and Applications  
Multimodal emotion recognition is based on decision-level fusion. The performance of emotion recognition algorithm is compared with the validation of human decision makers.  ...  Emotional speech is represented by commonly known audio and spectral features as well as MFCC coefficients. The SVM algorithm has been used for classification.  ...  The authors propose a novel Bayesian nonparametric multimodal data modeling framework using features extracted from key frames of video via convolutional neural networks (CNNs) and Mel-frequency cepstral  ... 
doi:10.1007/s00138-018-0960-9 fatcat:vsr3szjuanf3phcwoiwe2s3dxm

Context-Aware Attention Network for Human Emotion Recognition in Video

Xiaodong Liu, Miao Wang, Constantine Kotropoulos
2020 Advances in Multimedia  
In this paper, we first build a video dataset with seven categories of human emotion, named human emotion in the video (HEIV).  ...  Recognition of human emotion from facial expression is affected by distortions of pictorial quality and facial pose, which is often ignored by traditional video emotion recognition methods.  ...  [23] proposed a Bayesian nonparametric multimodal data modeling framework to learn emotions in videos, but it does not reflect the time evolution of emotional expression in videos. Kahou et al.  ... 
doi:10.1155/2020/8843413 fatcat:jt6ufgxbyjdq7fefy2n5cnvci4

Words Can Shift: Dynamically Adjusting Word Representations Using Nonverbal Behaviors

Yansen Wang, Ying Shen, Zhun Liu, Paul Pu Liang, Amir Zadeh, Louis-Philippe Morency
2019 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Our proposed model achieves competitive performance on two publicly available datasets for multimodal sentiment analysis and emotion recognition.  ...  As a result, when modeling human language, it is essential to not only consider the literal meaning of the words but also the nonverbal contexts in which these words appear.  ...  We also thank the anonymous reviewers for useful feedback.  ... 
doi:10.1609/aaai.v33i01.33017216 fatcat:cx22rdjwbncf7hpqar6fif6uqe

Words Can Shift: Dynamically Adjusting Word Representations Using Nonverbal Behaviors [article]

Yansen Wang, Ying Shen, Zhun Liu, Paul Pu Liang, Amir Zadeh, Louis-Philippe Morency
2018 arXiv   pre-print
Our proposed model achieves competitive performance on two publicly available datasets for multimodal sentiment analysis and emotion recognition.  ...  As a result, when modeling human language, it is essential to not only consider the literal meaning of the words but also the nonverbal contexts in which these words appear.  ...  We also thank the anonymous reviewers for useful feedback.  ... 
arXiv:1811.09362v2 fatcat:t6lih6egejcwvgdjzli5jnmyda

A review of speech-based bimodal recognition

C.C. Chibelushi, F. Deravi, J.S.D. Mason
2002 IEEE transactions on multimedia  
Multimodal recognition is therefore acknowledged as a vital component of the next generation of spoken language systems.  ...  Speech recognition and speaker recognition by machine are crucial ingredients for many important applications such as natural and flexible human-machine interfaces.  ...  Neural networks, Bayesian inference, Dempster-Shafer theory, and possibility theory have also provided frameworks for decision fusion [22] , [68] .  ... 
doi:10.1109/6046.985551 fatcat:6fezo5zovbdtti3lzxh24ksaii

Learning probabilistic classifiers for human–computer interaction applications

Nicu Sebe, Ira Cohen, Fabio G. Cozman, Theo Gevers, Thomas S. Huang
2005 Multimedia Systems  
In this paper, we discuss training probabilistic classifiers with labeled and unlabeled data for HCI applications.  ...  type of probabilistic classifiers, Bayesian networks.  ...  What is needed is a science of human-computer communication that establishes a framework for multimodal "language" and "dialog," much like the framework we have evolved for spoken exchange.  ... 
doi:10.1007/s00530-005-0177-4 fatcat:elku2ivr2bczvkbeuvxkyhvsei

Multimodal Machine Learning: A Survey and Taxonomy [article]

Tadas Baltrušaitis, Chaitanya Ahuja, Louis-Philippe Morency
2017 arXiv   pre-print
Multimodal machine learning aims to build models that can process and relate information from multiple modalities.  ...  Instead of focusing on specific multimodal applications, this paper surveys the recent advances in multimodal machine learning itself and presents them in a common taxonomy.  ...  [223] used LSTM models for continuous multimodal emotion recognition, demonstrating its advantage over graphical models and SVMs. Similarly, Nicolaou et al.  ... 
arXiv:1705.09406v2 fatcat:262fo4sihffvxecg4nwsifoddm

Symbol Emergence in Robotics: A Survey [article]

Tadahiro Taniguchi, Takayuki Nagai, Tomoaki Nakamura, Naoto Iwahashi, Tetsuya Ogata, Hideki Asoh
2015 arXiv   pre-print
Specifically, we describe some state-of-art research topics concerning SER, e.g., multimodal categorization, word discovery, and a double articulation analysis, that enable a robot to obtain words and  ...  Recently, many studies have been conducted on the construction of robotic systems and machine-learning methods that can learn the use of language through embodied multimodal interaction with their environment  ...  Bayesian nonparametrics is a branch of the Bayesian approach.  ... 
arXiv:1509.08973v1 fatcat:yg6bscvy2fdpdhapltyonvhs2a

Character index

2011 2011 IEEE International Conference on Multimedia and Expo  
MODELLING OF OUTLIERS FOR FAST VISUAL SEARCH Olivier Guye SKYMEDIA -UAV-BASED CAPTURING OF HD/3D CONTENT WITH WSN AUGMENTATION FOR IMMERSIVE MEDIA EXPERIENCES H Matthias Haase A PROCESSING TOOL FOR EMOTIONALLY  ...  FORMANTS ANALYSIS ALLOWS STRAIGHTFORWARD DETECTION OF HIGH AROUSAL EMOTIONS A PROCESSING TOOL FOR EMOTIONALLY COLOURED SPEECH Christophe De Vleeschouwer GRAPH-BASED FILTERING OF BALLISTIC TRAJECTORY AUTOMATIC  ... 
doi:10.1109/icme.2011.6011827 fatcat:wjy7yvkmvbbf3hj4wbyjapx5gu

Learning Factorized Multimodal Representations [article]

Yao-Hung Hubert Tsai and Paul Pu Liang and Amir Zadeh and Louis-Philippe Morency and Ruslan Salakhutdinov
2019 arXiv   pre-print
In this paper, we propose to optimize for a joint generative-discriminative objective across multimodal data and labels.  ...  Modality-specific generative factors are unique for each modality and contain the information required for generating data.  ...  -↑ 0.022 Table 1 : 1 Results for multimodal speaker traits recognition on POM, multimodal sentiment analysis on CMU-MOSI, ICT-MMMO, YouTube, MOUD, and multimodal emotion recognition on IEMOCAP.  ... 
arXiv:1806.06176v3 fatcat:7s4ero4yyfetpmg3mboojbgabu

COLD Fusion: Calibrated and Ordinal Latent Distribution Fusion for Uncertainty-Aware Multimodal Emotion Recognition [article]

Mani Kumar Tellamekala, Shahin Amiriparian, Björn W. Schuller, Elisabeth André, Timo Giesbrecht, Michel Valstar
2022 arXiv   pre-print
Automatically recognising apparent emotions from face and voice is hard, in part because of various sources of uncertainty, including in the input data and the labels used in a machine learning framework  ...  Our evaluation on two emotion recognition corpora, AVEC 2019 CES and IEMOCAP, shows that audiovisual emotion recognition can considerably benefit from well-calibrated and well-ranked latent uncertainty  ...  We further propose a multimodal fusion framework based on probabilistic modelling of unimodal temporal context.  ... 
arXiv:2206.05833v1 fatcat:7skw5owwpndkdgwrbmlymwwexu

A comprehensive study of visual event computing

WeiQi Yan, Declan F. Kieran, Setareh Rafatirad, Ramesh Jain
2010 Multimedia tools and applications  
We start by presenting events and their classifications, and continue with discussing the problem of capturing events in terms of photographs, videos, etc, as well as the methodologies for event storing  ...  This paper contains a survey on aspects of visual event computing.  ...  This work was partially supported by QUB research project: Unusual event detection in audio-visual surveillance for public transport (NO.D6223EEC).  ... 
doi:10.1007/s11042-010-0560-9 fatcat:ak6u3eefefgjhmbpr7asru3n7u

Table of Contents

2021 IEEE Signal Processing Letters  
Feng Feature Fusion for Multimodal Emotion Recognition Based on Deep Canonical Correlation Analysis . . . . . . . . . . . . . . . . .  ...  Yu Multimodal Embeddings From Language Models for Emotion Recognition in the Wild . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/lsp.2021.3134549 fatcat:m6obtl7k7zdqvd62eo3c4tptfy

AI's 10 to Watch

James Hendler, Philipp Cimiano, Dmitri Dolgov, Anat Levin, Peter Mika, Brian Milch, Louis-Philippe Morency, Boris Motik, Jennifer Neville, Erik B. Sudderth, Luis von Ahn
2008 IEEE Intelligent Systems  
His research interests include probabilistic graphical models; nonparametric Bayesian methods; and applications of statistical machine learning in image processing, tracking, object recognition, and visual  ...  Applying these nonparametric Bayesian methods, I've developed hierarchical generative models for objects, the parts composing them, and the scenes surrounding them.  ... 
doi:10.1109/mis.2008.40 fatcat:nz7bdn2torfthilyflnwmmbmc4

2021 Index IEEE/ACM Transactions on Audio, Speech, and Language Processing Vol. 29

2021 IEEE/ACM Transactions on Audio Speech and Language Processing  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  ., +, TASLP 2021 2339-2350 Multimodal Emotion Recognition With Temporal and Semantic Consis-tency.  ...  Wang, X., +, TASLP 2021 850-865 Multimodal Emotion Recognition With Temporal and Semantic Consis-tency.  ... 
doi:10.1109/taslp.2022.3147096 fatcat:7nl52k7sjfalbhpxtum3y5nmje
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