Abstract Background: Natural Language Processing (NLP) is widely used to extract clinical
insights from Electronic Health Records (EHRs). However, the lack of annotated data …

Emotion recognition has recently attracted extensive interest due to its significant
applications to human–computer interaction. The expression of human emotion depends on …

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

Speech emotion recognition (SER) as a Machine Learning (ML) problem continues to
garner a significant amount of research interest, especially in the affective computing …

Affective computing is one of the most important research fields in modern human–computer
interaction (HCI). The goal of affective computing is to study and develop the theories …

The remarkable success of transformers in the field of natural language processing has
sparked the interest of the speech-processing community, leading to an exploration of their …

The speech emotion recognition (SER) field has been active since it became a crucial
feature in advanced Human–Computer Interaction (HCI), and wide real-life applications use …

Using ECG signals captured by wearable devices for emotion recognition is a feasible
solution. We propose a deep convolutional neural network incorporating attentional …

The emergence of artificial emotional intelligence technology is revolutionizing the fields of
computers and robotics, allowing for a new level of communication and understanding of …

With the continuous development of deep learning (DL), the task of multimodal dialog
emotion recognition (MDER) has recently received extensive research attention, which is …