Speech is the most natural way of expressing ourselves as humans. It is only natural then to
extend this communication medium to computer applications. We define speech emotion …

Speech emotion recognition (SER) is traditionally performed using merely acoustic
information. Acoustic features, commonly are extracted per frame, are mapped into emotion …

User emotional status recognition is becoming a key feature in advanced Human Computer
Interfaces (HCI). A key source of emotional information is the spoken expression, which may …

Speech emotion recognition (SER) classifies speech into emotion categories such as:
Happy, Angry, Sad and Neutral. Recently, deep learning has been applied to the SER task …

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 …

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 emotion recognition involves analyzing vocal changes caused by emotions with
acoustic analysis and determining the features to be used for emotion recognition. The …

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 primitive descriptions are an important alternative to classical emotion categories
for describing a human's affective expressions. We build a multi-dimensional emotion space …

The most common approaches to automatic emotion recognition rely on utterance-level
prosodic features. Recent studies have shown that utterance-level statistics of segmental …