Speech emotion recognition (SER) as a Machine Learning (ML) problem continues to
garner a significant amount of research interest, especially in the affective computing …

Speech emotion recognition (SER) is a pivotal technology for human-computer interaction
systems. However, 80.77% of SER papers yield results that cannot be reproduced. We …

Abstract Automatic Speech Emotion Recognition (ASER) has recently garnered attention
across various fields including artificial intelligence, pattern recognition, and human …

Speech emotion recognition (SER) is a field that has drawn a lot of attention due to its
applications in diverse fields. A current trend in methods used for SER is to leverage …

Pre-trained models (PTMs) have shown great promise in the speech and audio domain.
Embeddings leveraged from these models serve as inputs for learning algorithms with …

Speech emotion recognition (SER) is an essential technology for human-computer
interaction systems. However, the previous study reveals that 80.77% of SER papers yield …

Dimensional representations of speech emotions such as the arousal-valence (AV)
representation provide a continuous and fine-grained description and control than their …

Research on diagnosing diseases based on voice signals is rapidly increasing, including
cough-related diseases. When training the cough sound signals into deep learning models …

Research on speech emotion recognition has been actively conducted; most are in
monolingual settings. Considering that emotion expressed in speech is universal, it is …

The rapid growth of Speech Emotion Recognition (SER) has diverse global applications,
from improving human-computer interactions to aiding mental health diagnostics. However …