IEEE Transactions on Affective Computing

The papers in this special section focus on affective speech and language synthesis, generation, and conversion. As an inseparable and crucial part of spoken language, emotions play a substantial role in human-human and human-technology conversation. They ...

Whether a word was bawled, whispered, or yelped, captions will typically represent it in the same way. If they are your only way to access what is being said, subjective nuances expressed in the voice will be lost. Since so much of communication is ...

Emotion is important for the conversational user interface. In prior research, conversational agents (CAs) employ natural language process techniques to create affective interaction based on text. However, the use of acoustic features of speech for voice-...

Emotional voice conversion (EVC) seeks to convert the emotional state of an utterance while preserving the linguistic content and speaker identity. In EVC, emotions are usually treated as discrete categories overlooking the fact that speech also conveys ...

Textual language is the most natural carrier of human emotion. In natural language processing, textual emotion recognition (TER) has become an important topic due to its significant academic and commercial potential. With the advanced development of deep ...

The design of meaningful audio features is a key need to advance the state-of-the-art in music emotion recognition (MER). This article presents a survey on the existing emotionally-relevant computational audio features, supported by the music psychology ...

This article aims to summarize and describe research on the topic of automatic group emotion recognition. In recent years, the topic of emotion analysis of groups or crowds has gained interest, with studies performing emotion detection in different ...

Sentiment analysis as a field has come a long way since it was first introduced as a task nearly 20 years ago. It has widespread commercial applications in various domains like marketing, risk management, market research, and politics, to name a few. ...

Given the prevalence of depression worldwide and its major impact on society, several studies employed artificial intelligence modelling to automatically detect and assess depression. However, interpretation of these models and cues are rarely discussed ...

Recent statistics in suicide prevention show that people are increasingly posting their last words online and with the unprecedented availability of textual data from social media platforms researchers have the opportunity to analyse such data. ...

Affective factors have been associated with an array of driving behaviors. However, the mechanism by which emotion influences driving behaviors remains largely unknown. In the present study, a probabilistic approach for characterizing the emotional ...

This article aims to solve two important issues that frequently occur in existing automatic personality analysis systems: 1. Attempting to use very short video segments or even single frames, rather than long-term behaviour, to infer personality traits; ...

Aspect-level sentiment classification aims to determine the sentiment polarity of a sentence toward a given aspect term or aspect category. For sentiment classification toward a given aspect term, some opinions may exist that are not the given aspect term&...

Millions of people around the world are affected by one or more mental disorders that interfere in their thinking and behavior. A timely detection of these issues is challenging but crucial, since it could open the possibility to offer help to people ...

In this article, we describe the iterative participatory design of SOPHIE, an online virtual patient for feedback-based practice of sensitive patient-physician conversations, and discuss an initial qualitative evaluation of the system by professional end ...

We propose an automatic ternary classification model for Bipolar Disorder (BD) states. As input information, the model uses speech signals from patients’ audio-visual recordings of structured interviews. The model classifies the patient's ...

In this article, we introduce a new neonatal facial expression database for pain analysis. This database, called facial expression of neonatal pain (FENP), contains 11,000 neonatal facial expression images associated with 106 Chinese neonates from two ...

After 20 years of testing a framework for affective user responses to artificial agents and robots, we compiled a full formalization of our findings so to make the agent respond affectively to its user. Silicon Coppélia as we dubbed our system ...

The state-of-the-art approaches to targeted aspect-based sentiment analysis (TABSA) are mostly built on deep neural networks with attention mechanisms. One problem is that embeddings of targets and aspects are either pre-trained from large external ...

Physiological studies have shown that there are some differences in speech and facial activities between depressive and healthy individuals. Based on this fact, we propose a novel spatio-temporal attention (STA) network and a multimodal attention feature ...

People often prefer videos over images in research and applications, believing that videos are more effective for eliciting human emotions and building machine intelligence. However, our research shows that this assumption is not always correct when it ...

Classification of interbeat interval time-series which fluctuates in an irregular and complex manner is very challenging. Typically, entropy methods are employed to quantify the complexity of the time-series for classifying. Traditional entropy methods ...

Interpersonal touch is critical for social-emotional development and presents a powerful modality for communicating emotions. Virtual agents of the future could capitalize on touch to establish social bonds with humans and facilitate cooperation in ...

The individual differences and the dynamic uncertain relationships among different electroencephalogram (EEG) regions are essential factors that limit EEG emotion recognition. To address these issues, in this article, we propose a variational instance-...

Current works on image emotion distribution learning typically extract visual representations from the holistic image or explore emotion-related regions in the image from a global-wise perspective. However, different regions of an image contribute ...

Compared with the widely studied negative emotions in which different classes are easy to distinguish, nowadays less attention is paid to the recognition of positive emotions that are not fully independent. In this article, we propose to recognize ...

Emotion recognition based on electroencephalography (EEG) is a significant task in the brain-computer interface field. Recently, many deep learning-based emotion recognition methods are demonstrated to outperform traditional methods. However, it remains ...

Adaptively increasing the difficulty level in learning was shown to be beneficial than increasing the level after some fixed time intervals. To efficiently adapt the level, we aimed at decoding the subjective difficulty level based on ...

Self-supervised learning has attracted plenty of recent research interest. However, most works for self-supervision in speech are typically unimodal and there has been limited work that studies the interaction between audio and visual modalities for cross-...