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

Leadership scholars increasingly acknowledge the shortcomings of using questionnaires.
Consequently, there is a trend towards more behavior-based research, with interaction …

Emotion recognition in conversations is crucial for the development of empathetic machines.
Present methods mostly ignore the role of inter-speaker dependency relations while …

We propose a multi-task ensemble framework that jointly learns multiple related problems.
The ensemble model aims to leverage the learned representations of three deep learning …

Automatic speech emotion recognition provides computers with critical context to enable
user understanding. While methods trained and tested within the same dataset have been …

Many mobile applications and virtual conversational agents now aim to recognize and adapt
to emotions. To enable this, data are transmitted from users' devices and stored on central …

We propose a deep graph approach to address the task of speech emotion recognition. A
compact, efficient and scalable way to represent data is in the form of graphs. Following the …

Recent years have witnessed the great success of deep learning models in semantic
segmentation. Nevertheless, these models may not generalize well to unseen image …

Automatic speech emotion recognition (SER) by a computer is a critical component for more
natural human-machine interaction. As in human-human interaction, the capability to …

Emotion recognition is a challenging and actively-studied research area that plays a critical
role in emotion-aware human-computer interaction systems. In a multimodal setting …