Recent advances in transformer-based architectures have shown promise in several
machine learning tasks. In the audio domain, such architectures have been successfully …

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 …

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

Receiving an accurate emotional response from robots has been a challenging task for
researchers for the past few years. With the advancements in technology, robots like service …

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) …

An important part of the human-computer interaction process is speech emotion recognition
(SER), which has been receiving more attention in recent years. However, although a wide …

Speech emotion recognition (SER) is still a fresh in natural language processing domain
since the accuracy is beyond targeted. Mainly due to real-time applications such as human …

Despite the recent progress in speech emotion recognition (SER), state-of-the-art systems
lack generalisation across different conditions. A key underlying reason for poor …

Large, pre-trained neural networks consisting of self-attention layers (transformers) have
recently achieved state-of-the-art results on several speech emotion recognition (SER) …

A speech emotion recognition (SER) system deployed on a real-world application can
encounter speech contaminated with unconstrained background noise. To deal with this …