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 …

Speech carries essential information about the speaker's physiology and possible
pathophysiological conditions. Bio-acoustic voice qualities show promising value for …

Automatic emotion recognition from speech is a challenging task which relies heavily on the
effectiveness of the speech features used for classification. In this work, we study the use of …

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 …

Speaker emotion recognition is achieved through processing methods that include isolation
of the speech signal and extraction of selected features for the final classification. In terms of …

Depression has been the leading cause of mental-health illness worldwide. Major
depressive disorder (MDD), is a common mental health disorder that affects both …

In this study, modulation spectral features (MSFs) are proposed for the automatic recognition
of human affective information from speech. The features are extracted from an auditory …

The objective of this paper is to present the state of-the-art relating to automatic speech and
voice analysis techniques as applied to the monitoring of patients suffering from Alzheimer's …

As the recognition of emotion from speech has matured to a degree where it becomes
applicable in real-life settings, it is time for a realistic view on obtainable performances. Most …

Automated emotion state tracking is a crucial element in the computational study of human
communication behaviors. It is important to design robust and reliable emotion recognition …