In this paper, we propose a novel method for highly efficient exploitation of unlabeled data-
Cooperative Learning. Our approach consists of combining Active Learning and Semi …

Speech emotion recognition is an important task with a wide range of applications. However,
the progress of speech emotion recognition is limited by the lack of large, high-quality …

When recognizing emotions from speech, we encounter two common problems: how to
optimally capture emotion-relevant information from the speech signal and how to best …

Despite the widespread use of supervised learning methods for speech emotion recognition,
they are severely restricted due to the lack of sufficient amount of labelled speech data for …

This study introduces a method to design a curriculum for machine-learning to maximize the
efficiency during the training process of deep neural networks (DNNs) for speech emotion …

There is growing interest in emotion recognition due to its potential in many applications.
However, a pervasive challenge is the presence of data variability caused by factors such as …

Research on automatic emotion recognition from speech has recently focused on the
prediction of time-continuous dimensions (eg, arousal and valence) of spontaneous and …

Obtaining large, human labelled speech datasets to train models for emotion recognition is a
notoriously challenging task, hindered by annotation cost and label ambiguity. In this work …

Abstract Speech Emotion Recognition (SER) has been shown to benefit from many of the
recent advances in deep learning, including recurrent based and attention based neural …

Speech Emotion Recognition (SER) application is frequently associated with privacy
concerns as it often acquires and transmits speech data at the client-side to remote cloud …