Article
Business
Lieze Schoofs, An-Sofie Claeys
Summary: In a crisis, both verbal and visual expressions of sadness can increase public empathy towards the CEO, positively affecting organizational reputation. However, expressing sadness may also decrease perceptions of the CEO's competence, which is detrimental to organizational reputation.
JOURNAL OF BUSINESS RESEARCH
(2021)
Article
Dentistry, Oral Surgery & Medicine
Guido Artemio Maranon-Vasquez, Lucianne Cople Maia, Luisa Schubach da Costa Barreto, Mariana Farias da Cruz, Lucas Alves Jural, Monica Tirre de Souza Araujo, Matheus Melo Pithon
Summary: This study preliminarily evaluated the discriminating ability and relationship of Emoji in different occlusal conditions/malocclusions and found that they have an adequate discriminatory ability and can be used to determine emotional profiles.
PROGRESS IN ORTHODONTICS
(2022)
Article
Engineering, Electrical & Electronic
Dongrui Wu, Bao-Liang Lu, Bin Hu, Zhigang Zeng
Summary: A brain-computer interface (BCI) allows direct communication between a user and a computer through the central nervous system. An affective BCI (aBCI) monitors and regulates the emotional state of the brain, which has various applications in human cognition, communication, decision-making, and health. This tutorial provides a comprehensive and up-to-date guide on aBCIs, covering basic concepts, components of a closed-loop aBCI system, representative applications, and challenges and opportunities in aBCI research and applications.
PROCEEDINGS OF THE IEEE
(2023)
Article
Psychology, Multidisciplinary
Sylwia Hyniewska, Joanna Dabrowska, Iwona Makowska, Kamila Jankowiak-Siuda, Krystyna Rymarczyk
Summary: People with borderline personality disorder (iBPD) tend to interpret negative emotions related to social rejection more accurately and negatively compared to healthy controls, with contempt being recognized better by iBPD than controls.
FRONTIERS IN PSYCHOLOGY
(2021)
Review
Computer Science, Artificial Intelligence
Sicheng Zhao, Xingxu Yao, Jufeng Yang, Guoli Jia, Guiguang Ding, Tat-Seng Chua, Bjorn W. Schuller, Kurt Keutzer
Summary: This survey comprehensively reviews the development of affective image content analysis (AICA) in the past two decades, focusing on the state-of-the-art methods and addressing three main challenges. It provides an overview of emotion representation models, available datasets, and compares representative approaches in emotion feature extraction, learning methods, and AICA-based applications. The survey also discusses future research directions and challenges in the field.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Jaya Narain, Kristina T. Johnson, Thomas F. Quatieri, Rosalind W. Picard, Pattie Maes
Summary: Nonverbal vocalizations from non- and minimally speaking individuals convey important communicative and affective information. This study is among the first to examine the communicative and affective information expressed in nonverbal vocalizations by these individuals. The researchers collected labeled vocalizations in real-world settings and were able to classify them by function with high accuracy.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2022)
Article
Psychology, Multidisciplinary
Isabelle Boutet, Megan LeBlanc, Justin A. Chamberland, Charles A. Collin
Summary: The study found that emojis have an impact on emotion interpretation and information processing. Negative emojis intensify negativity, while positive emojis enhance perceived warmth and understanding. Emojis, especially positive ones, are useful for improving communication and making a positive impression in digital interactions.
COMPUTERS IN HUMAN BEHAVIOR
(2021)
Article
Psychology, Experimental
Fangyun Zhao, Adrienne Wood, Bilge Mutlu, Paula Niedenthal
Summary: Cooperating with another person requires communication and coordination, typically achieved through spoken language. However, when verbal communication is not available, people compensate by synchronizing their facial expressions.
Article
Computer Science, Artificial Intelligence
Desmond C. Ong, Harold Soh, Jamil Zaki, Noah D. Goodman
Summary: Affective Computing is a fast-growing field that proposes a probabilistic programming approach to translate psychological theories of emotion into computational models. Probabilistic programming languages offer flexibility, modularity, integration with deep learning libraries, and ease of adoption, providing a standardized platform for theory-building and experimentation.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2021)
Article
Psychology, Multidisciplinary
Lillian Dollinger, Petri Laukka, Lennart Bjorn Hogman, Tanja Banziger, Irena Makower, Hakan Fischer, Stephan Hau
Summary: Nonverbal emotion recognition accuracy (ERA) is essential for successful communication, and two different training programs focusing on multimodal expressions and micro expressions respectively were evaluated. Results showed that the training program focusing on multimodal expressions was more effective in improving overall ERA, while the one focusing on micro expressions was more effective in improving micro expression ERA specifically. Transfer effects of the training programs were not observed, and participants with lower baseline ERA showed more improvements.
FRONTIERS IN PSYCHOLOGY
(2021)
Article
Chemistry, Analytical
Fangfang Zhu-Zhou, Roberto Gil-Pita, Joaquin Garcia-Gomez, Manuel Rosa-Zurera
Summary: This study demonstrates through experiments that emotional speech databases generated under non-real-world conditions fail to provide data close enough to real scenarios, leading to a decline in the performance of emotion recognition systems. The paper also proposes a virtual enlargement method and a robust multi-scenario speech-based emotion recognition system.
Review
Psychiatry
Celine Ramdani, Michael Ogier, Antoine Coutrot
Summary: Face masks are effective in slowing down the spread of the SARS-Cov2 virus during the COVID-19 pandemic, but they have side effects on both physical and psychosocial levels. They hinder emotion reading and communication, especially for individuals with psychiatric conditions. It is important to acknowledge these side effects and work on adaptive solutions to encourage widespread use of face masks.
PSYCHIATRY RESEARCH
(2022)
Article
Psychology, Experimental
Thorsten M. Erle, Karoline Schmid, Simon H. Goslar, Jared D. Martin
Summary: This research examined the effectiveness of emojis in conveying emotions and disambiguating messages in digital communication. Emojis were found to have similar effects to facial expressions in face-to-face communication, instigating emotional and inferential processes that led to behavioral intentions. The study suggests that emojis serve as effective quasi-nonverbal cues in digital communication, supporting the Emotion as Social Information (EASI) model.
Article
Computer Science, Information Systems
Fei Yan, Abdullah M. Iliyasu, Kaoru Hirota
Summary: This study aims to interpret and manipulate robots' emotions within the framework of quantum mechanics, encoding emotion information as superposition states and using unitary operators to manipulate emotion transitions. Fusion of multi-robots' emotions through quantum entanglement reduces the qubit requirements and quantum gate usage, demonstrating the feasibility and effectiveness of the proposed framework in transitioning emotional intelligence formulations to the quantum era.
Review
Computer Science, Artificial Intelligence
Renan Vinicius Aranha, Cleber Gimenez Correa, Fatima L. S. Nunes
Summary: The study focuses on the impact of Affective Computing in promoting user engagement in computer applications, with a systematic literature review of available articles discussing emotion recognition techniques and challenges to be overcome in the field.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2021)
Article
Psychology, Developmental
S. Hampton, C. Allison, S. Baron-Cohen, R. Holt
Summary: There are sensory and communication related barriers to childbirth and postnatal healthcare for autistic people, with a need for adjustments and greater mental health support.
JOURNAL OF AUTISM AND DEVELOPMENTAL DISORDERS
(2023)
Article
Psychology, Developmental
Sarah Hampton, Joyce Man, Carrie Allison, Ezra Aydin, Simon Baron-Cohen, Rosemary Holt
Summary: Pregnant autistic women may experience heightened sensory and physical symptoms during pregnancy, and may hesitate to disclose their diagnosis to healthcare professionals due to perceived lack of knowledge. They require detailed information and time to process verbal information, and also emphasize the need for sensory adjustments in healthcare settings.
Article
Psychology, Developmental
Ofer Golan, Michael Terner, Sandra Israel-Yaacov, Carrie Allison, Simon Baron-Cohen
Summary: The study examined the properties of the Hebrew version of the Autism-Spectrum Quotient and created a short version suitable for autism assessment. The Hebrew version showed good internal consistency and had high sensitivity and specificity, making it an effective tool for screening autistic traits in clinically referred adults.
Article
Biochemistry & Molecular Biology
Ravi Prabhakar More, Varun Warrier, Helena Brunel, Clara Buckingham, Paula Smith, Carrie Allison, Rosemary Holt, Charles R. Bradshaw, Simon Baron-Cohen
Summary: This study conducted whole-genome sequencing of 21 highly multiplex autism families and identified rare variants in genes associated with autism. The study also found a convergence of the genes identified in molecular pathways related to development and neurogenesis. These findings provide initial evidence to demonstrate the value of integrating autism diagnosis and autistic traits to prioritize genes.
MOLECULAR PSYCHIATRY
(2023)
Article
Multidisciplinary Sciences
Emilia Parada-Cabaleiro, Anton Batliner, Maximilian Schmitt, Markus Schedl, Giovanni Costantini, Bjoern Schuller
Summary: This article addresses four fallacies in traditional affective computing and proposes a more adequate modelling of emotions encoded in speech. The fallacies include limited focus on few emotions, lack of comparison between clean and noisy data, insufficient assessment of machine learning approaches, and the absence of strict comparison between human perception and machine classification. The article demonstrates the effectiveness of machine learning based on state-of-the-art feature representations in reflecting the main emotional categories even in degraded acoustic conditions.
Editorial Material
Computer Science, Cybernetics
Kun Qian, Bjorn W. Schuller, Xiaohong Guan, Bin Hu
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2023)
Article
Engineering, Biomedical
Zhihua Wang, Kun Qian, Houguang Liu, Bin Hu, Bjorn W. Schuller, Yoshiharu Yamamoto
Summary: The advantages of non-invasive, real-time and convenient computer audition-based heart sound abnormality detection methods have attracted increasing attention from the cardiovascular diseases community. A comprehensive investigation on time-frequency methods for analyzing heart sounds is proposed, considering the urgent need for robust detection algorithms in real environments. Experimental results show that Stockwell transformation outperforms other methods with the highest overall score of 65.2%, and the interpretable results demonstrate its ability to provide more information and noise robustness for heart sounds.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Computer Science, Artificial Intelligence
Sebastian P. Bayerl, Maurice Gerczuk, Anton Batliner, Christian Bergler, Shahin Amiriparian, Bjoern Schuller, Elmar Noeth, Korbinian Riedhammer
Summary: The ACM Multimedia 2022 Computational Paralinguistics Challenge (ComParE) focused on the classification of stuttering, aiming to raise awareness and engage a wider research community. Stuttering is a complex speech disorder characterized by blocks, prolongations, and repetitions in speech. Accurate classification of stuttering symptoms is important for the development of self-help tools and specialized automatic speech recognition systems. This paper reviews the challenge contributions, presents improved state-of-the-art classification results, and explores cross-language training using the KSF-C dataset.
COMPUTER SPEECH AND LANGUAGE
(2023)
Article
Computer Science, Artificial Intelligence
Lukas Stappen, Alice Baird, Lea Schumann, Bjoern Schuller
Summary: Truly real-life data presents a challenge for sentiment and emotion research. The large variety of 'in-the-wild' properties necessitates the use of large datasets for building robust machine learning models. This paper introduces MuSe-CaR, a first-ever multimodal dataset, and provides a comprehensive overview of its collection and annotation. Furthermore, the paper proposes a Multi-Head-Attention network that outperforms the baseline model in predicting trustworthiness levels.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Mostafa M. Amin, Erik Cambria, Bjoern W. Schuller
Summary: The employment of foundation models is expanding and ChatGPT has the potential to enhance existing NLP techniques with its novel knowledge.
IEEE INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Johannes Wagner, Andreas Triantafyllopoulos, Hagen Wierstorf, Maximilian Schmitt, Felix Burkhardt, Florian Eyben, Bjoern W. W. Schuller
Summary: Recent advances in transformer-based architectures have shown promise in several machine learning tasks, specifically speech emotion recognition (SER) in the audio domain. However, existing works have not thoroughly evaluated the influence of model size and pre-training data on downstream performance, and have shown limited attention to generalisation, robustness, fairness, and efficiency. This study conducts a thorough analysis on pre-trained variants of wav2vec 2.0 and HuBERT, demonstrating their top performance for valence prediction without explicit linguistic information, and releasing the best performing model to the community for reproducibility.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Mingyue Niu, Ziping Zhao, Jianhua Tao, Ya Li, Bjorn Schuller
Summary: This paper proposes a method for predicting depression levels based on facial dynamics. The method uses a Dual Attention and Element Recalibration network to extract facial changes for prediction. Experimental results demonstrate the effectiveness of the method.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Decky Aspandi, Federico Sukno, Bjorn W. Schuller, Xavier Binefa
Summary: There is growing interest in automatic emotion recognition and affective computing. The use of large video-based affect datasets has facilitated the development of deep learning-based models for automatic affect analysis. However, current approaches to process these multimodal inputs are oversimplified and fail to fully exploit their potential. This work proposes a multi-modal, sequence-based neural network with gating mechanisms for affect recognition, achieving state of the art accuracy on two affect datasets.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2023)
Editorial Material
Computer Science, Artificial Intelligence
Frank Xing, Bjoern Schuller, Iti Chaturvedi, Erik Cambria, Amir Hussain
Summary: Neural network-based methods, such as word2vec and GPT-based models, have achieved significant progress in AI research, especially in handling large datasets. However, these methods lack in-depth understanding of the internal features and representations of the data, leading to various problems and concerns.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Siddique Latif, Rajib Rana, Sara Khalifa, Raja Jurdak, Bjorn Schuller
Summary: Despite recent advancements in speech emotion recognition (SER) within a single corpus, the performance of these systems degrades significantly for cross-corpus and cross-language scenarios. This is due to the lack of generalization in SER systems towards unseen conditions. Adversarial methods have been used to address this issue, but many only focus on cross-corpus SER and ignore the cross-language performance degradation. This study proposes an adversarial dual discriminator (ADDi) network and a self-supervised ADDi (sADDi) network to improve cross-corpus and cross-language SER without requiring target data labels. Experimental results demonstrate improved performance compared to state-of-the-art methods.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2023)
