The advancements in neural networks and the on-demand need for accurate and near real-
time Speech Emotion Recognition (SER) in human–computer interactions make it …

Background Human emotions greatly affect the actions of a person. The automated emotion
recognition has applications in multiple domains such as health care, e-learning …

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 …

Emotions are an integral part of human interactions and are significant factors in determining
user satisfaction or customer opinion. speech emotion recognition (SER) modules also play …

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

Precise recognition of emotion from speech signals aids in enhancing human–computer
interaction (HCI). The performance of a speech emotion recognition (SER) system depends …

Artificial intelligence, deep learning, and machine learning are dominant sources to use in
order to make a system smarter. Nowadays, the smart speech emotion recognition (SER) …

Generative adversarial networks (GANs) have seen remarkable progress in recent years.
They are used as generative models for all kinds of data such as text, images, audio, music …

Despite the recent advancement in speech emotion recognition (SER) within a single corpus
setting, the performance of these SER systems degrades significantly for cross-corpus and …

Audio-visual emotion recognition is the research of identifying human emotional states by
combining the audio modality and the visual modality simultaneously, which plays an …