More than a decade has passed since research on automatic recognition of emotion from
speech has become a new field of research in line with its 'big brothers' speech and speaker …

Work on voice sciences over recent decades has led to a proliferation of acoustic
parameters that are used quite selectively and are not always extracted in a similar fashion …

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 …

In this project, we aim to classify the speech taken as one of the four emotions namely,
sadness, anger, fear and happiness. The samples that have been taken to complete this …

In this work, we explore the use of speaker conditioning sub-networks for speaker
adaptation in a deep neural network (DNN) based speech emotion recognition (SER) …

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 …

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 …

One of the main barriers in the deployment of speech emotion recognition systems in real
applications is the lack of generalization of the emotion classifiers. The recognition …

This paper proposes an efficient feature vector classification for Speech Emotion
Recognition (SER) in service robots. Since service robots interact with diverse users who …