While speech emotion recognition (SER) has been an active research field since the last
three decades, the techniques that deal with the natural environment have only emerged in …

Emotions play an important role in the learning process. Considering the learner's emotions
is essential for electronic learning (e-learning) systems. Some researchers have proposed …

Speech emotion recognition is a challenging problem partly because it is unclear what
features are effective for the task. In this paper we propose to utilize deep neural networks …

In this paper, we propose a novel domain invariant feature learning (DIFL) method to deal
with speaker-independent speech emotion recognition (SER). The basic idea of DIFL is to …

Emotion recognition is the ability to identify what people would think someone is feeling from
moment to moment and understand the connection between his/her feelings and …

We introduce a ranking approach for emotion recognition which naturally incorporates
information about the general expressivity of speakers. We demonstrate that our approach …

Representation learning for speech emotion recognition is challenging due to labeled data
sparsity issue and lack of gold-standard references. In addition, there is much variability from …

Cross-corpus speech emotion recognition has recently received considerable attention due
to the widespread existence of various emotional speech. It takes one corpus as the training …

Speech emotion recognition (SER) systems are often evaluated in a speaker-independent
manner. However, the variation in the acoustic features of different speakers used during …

This article analyses research in speech emotion recognition (“SER”) from 2006 to 2017 in
order to identify the current focus of research, and areas in which research is lacking. The …