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 …

We aimed at learning deep emotion features to recognize speech emotion. Two
convolutional neural network and long short-term memory (CNN LSTM) networks, one 1D …

This review condenses the knowledge on variable selection methods implemented in R and
appropriate for datasets with grouped features. The focus is on regularized regressions …

Human emotional expression tends to evolve in a structured manner in the sense that
certain emotional evolution patterns, ie, anger to anger, are more probable than others, eg …

Automatic recognition of emotion is important for facilitating seamless interactivity between a
human being and intelligent robot towards the full realization of a smart society. The …

Proposing a practical method for high-performance emotion recognition could facilitate
human–computer interaction. Among existing methods, deep learning techniques have …

Features greatly influence the results of speech emotion recognition, among which Mel-
frequency Cepstral Coefficients (MFCC) is the most commonly used in speech emotion …

There is growing amount of scientific evidence that motor activity is the most consistent
indicator of bipolar disorder. Motor activity includes several areas such as body movement …

The speech signal consists of linguistic information and also paralinguistic one such as
emotion. The modern automatic speech recognition systems have achieved high …

One of the main challenges in emotion recognition from speech is to discriminate emotions
in the valence domain (positive versus negative). While acoustic features provide good …