In recent years, an enormous amount of research has been carried out on support vector
machines (SVMs) and their application in several fields of science. SVMs are one of the …

In recent years, the rapid advances in machine learning (ML) and information fusion has
made it possible to endow machines/computers with the ability of emotion understanding …

Humans perceive the world by concurrently processing and fusing high-dimensional inputs
from multiple modalities such as vision and audio. Machine perception models, in stark …

One of the main challenges facing the current approaches of speech emotion recognition is
the lack of a dataset large enough to train the currently available deep learning models …

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 …

Over the past decades, a tremendous amount of research has been done on the use of
machine learning for speech processing applications, especially speech recognition …

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 …

Abstract We present Masked Audio-Video Learners (MAViL) to learn audio-visual
representations with three complementary forms of self-supervision:(1) reconstructing …

Automatic human affect recognition is a key step towards more natural human-computer
interaction. Recent trends include recognition in the wild using a fusion of audiovisual and …

Multimodal representation learning, which aims to narrow the heterogeneity gap among
different modalities, plays an indispensable role in the utilization of ubiquitous multimodal …