Speech emotion recognition (SER) systems identify emotions from the human voice in the
areas of smart healthcare, driving a vehicle, call centers, automatic translation systems, and …

Recognition of human movements with radar for ambient activity monitoring is a developed
area of research that yet presents outstanding challenges to address. In real environments …

This paper focuses on the development of shallow and deep neural networks in the form of
multi-layer perceptron, long-short term memory, and gated recurrent unit to model the short …

In this paper, we propose a novel Electroencephalograph (EEG) emotion recognition
method inspired by neuroscience with respect to the brain response to different emotions …

The temporal and spatial information of electroencephalogram (EEG) are essential for the
emotion recognition model to learn the discriminative features. Hence, we propose a novel …

Biomedical named entity recognition (BNER), which extracts important named entities such
as genes and proteins, is a challenging task in automated systems that mine knowledge in …

Pronunciation assessment and its application in computer-aided pronunciation training
(CAPT) have seen impressive progress in recent years. With the rapid growth in language …

Self-supervised learning (SSL) approaches such as wav2vec 2.0 and HuBERT models have
shown promising results in various downstream tasks in the speech community. In particular …

Since convolutional neural networks (CNNs) achieved top performance on the ImageNet
task in 2012, deep learning has become the preferred approach to addressing computer …

This paper presents different information fusion approaches to classify human gait patterns
and falls in a radar sensors network. The human gaits classified in this work are both …