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

The omnipresence of numerous information sources in our daily life brings up new
alternatives for emotion recognition in several domains including e-health, e-learning …

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

In the past decade, convolutional neural networks (CNNs) have been widely adopted as the
main building block for end-to-end audio classification models, which aim to learn a direct …

In recent years, mobile devices are equipped with increasingly advanced sensing and
computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up …

Speech is the most natural way of expressing ourselves as humans. It is only natural then to
extend this communication medium to computer applications. We define speech emotion …

The speech emotion recognition (or, classification) is one of the most challenging topics in
data science. In this work, we introduce a new architecture, which extracts mel-frequency …

Emotion recognition from speech signals is an important but challenging component of
Human-Computer Interaction (HCI). In the literature of speech emotion recognition (SER) …

Analyzing human multimodal language is an emerging area of research in NLP. Intrinsically
this language is multimodal (heterogeneous), sequential and asynchronous; it consists of …

The advancements in neural networks and the on-demand need for accurate and near real-
time Speech Emotion Recognition (SER) in human–computer interactions make it …