Affective computing conjoins the research topics of emotion recognition and sentiment
analysis, and can be realized with unimodal or multimodal data, consisting primarily of …

Speech emotion recognition (SER) is traditionally performed using merely acoustic
information. Acoustic features, commonly are extracted per frame, are mapped into emotion …

Recent advances in transformer-based architectures have shown promise in several
machine learning tasks. In the audio domain, such architectures have been successfully …

Automatic emotion recognition is one of the central concerns of the Human-Computer
Interaction field as it can bridge the gap between humans and machines. Current works train …

With regard to human–machine interaction, accurate emotion recognition is a challenging
problem. In this paper, efforts were taken to explore the possibility to complete the feature …

Automated multimodal emotion recognition has become an emerging but challenging
research topic in the fields of affective learning and sentiment analysis. The existing works …

Automatic emotion recognition plays a key role in computer-human interaction as it has the
potential to enrich the next-generation artificial intelligence with emotional intelligence. It …

In this work, we propose a novel approach for multi-modal emotion recognition from
conversations using speech and text. The audio representations are learned jointly with a …

Various studies have confirmed the necessity and benefits of leveraging multimodal features
for SER, and the latest research results show that the temporal information captured by the …

Speech plays an important role among the human communication and also a dominant
source of medium for human computer interaction (HCI) to exchange information. Hence, it …