Emotion recognition is the ability to precisely infer human emotions from numerous sources
and modalities using questionnaires, physical signals, and physiological signals. Recently …

Emotions play a critical role in our daily lives, so the understanding and recognition of
emotional responses is crucial for human research. Affective computing research has mostly …

We aimed at learning deep emotion features to recognize speech emotion. Two
convolutional neural network and long short-term memory (CNN LSTM) networks, one 1D …

We exploit a self-supervised deep multi-task learning framework for electrocardiogram
(ECG)-based emotion recognition. The proposed solution consists of two stages of learning …

Speech emotion recognition is a vital and challenging task that the feature extraction plays a
significant role in the SER performance. With the development of deep learning, we put our …

Affective Computing has emerged as an important field of study that aims to develop
systems that can automatically recognize emotions. Up to the present, elicitation has been …

Artificial intelligence (AI) and machine learning (ML) are employed to make systems smarter.
Today, the speech emotion recognition (SER) system evaluates the emotional state of the …

Objective. Previous studies on emotion recognition from electroencephalography (EEG)
mainly rely on single-channel-based feature extraction methods, which ignore the functional …

Abstract Heart Rate Variability (HRV) is a good predictor of human health because the heart
rhythm is modulated by a wide range of physiological processes. This statement embodies …

The problem of recognition and classification of emotions in speech is one of the most
prominent research topics, that has gained popularity, in human-computer interaction in the …