During the last decade, Speech Emotion Recognition (SER) has emerged as an integral
component within Human-computer Interaction (HCI) and other high-end speech processing …

Speech technology is not appropriately explored even though modern advances in speech
technology—especially those driven by deep learning (DL) technology—offer …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Traditionally, speech emotion recognition (SER) research has relied on manually
handcrafted acoustic features using feature engineering. However, the design of …

Speech is the most dominant source of communication among humans, and it is an efficient
way for human–computer interaction (HCI) to exchange information. Nowadays, speech …

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 …

Speech emotion recognition (SER) is a challenging task since the distribution of the features
is different among various people. To improve generalization performance and accuracy of …

Speech emotion recognition (SER) is an active research field of digital signal processing
and plays a crucial role in numerous applications of Human–computer interaction (HCI) …

Communication has been an important aspect of human life, civilization, and globalization
for thousands of years. Biometric analysis, education, security, healthcare, and smart cities …