Active learning (AL) attempts to maximize a model's performance gain while annotating the
fewest samples possible. Deep learning (DL) is greedy for data and requires a large amount …

Among the other modes of communication, such as text, body language, facial expressions,
and so on, human beings employ speech as the most common. It contains a great deal of …

Speech emotion recognition (SER) systems find applications in various fields such as
healthcare, education, and security and defense. A major drawback of these systems is their …

Inverse modeling is usually necessary for prediction of subsurface flows, which is beneficial
to characterize underground geologic properties and reduce prediction uncertainty …

Speech emotion recognition (SER) is a natural method of recognizing individual emotions in
everyday life. To distribute SER models to real-world applications, some key challenges …

Retrieving speech samples that have specific expressive content has many applications. It is
desirable to build a preference learning framework that ranks speech samples according to …

This paper presents a novel speech emotion recognition (SER) method to capture the
uncertainty in predicting emotional attributes using the true distribution of scores provided by …

Despite the promising performance of automated pain assessment methods, current
methods suffer from performance generalization due to the lack of relatively large, diverse …

Deep clustering is a popular unsupervised technique for feature representation learning. We
recently proposed the chunk-based DeepEmoCluster framework for speech emotion …

Emotion recognition is an important part of affective computing. Human emotions can be
described categorically or dimensionally. Accurate machine learning models for emotion …