Affective computing conjoins the research topics of emotion recognition and sentiment
analysis, and can be realized with unimodal or multimodal data, consisting primarily of …

Emotions are an integral part of human interactions and are significant factors in determining
user satisfaction or customer opinion. speech emotion recognition (SER) modules also play …

Automatic speech emotion recognition using machine learning is a demanding research
topic in the field of affective computing. Identifying the speech features for speech emotion …

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 …

This paper proposes a speech emotion recognition method based on phoneme sequence
and spectrogram. Both phoneme sequence and spectrogram retain emotion contents of …

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

The speech emotion recognition (SER) field has been active since it became a crucial
feature in advanced Human-Computer Interaction (HCI), and wide real-life applications use …

This work explores the use of constant-Q transform based modulation spectral features (CQT-
MSF) for speech emotion recognition (SER). The human perception and analysis of sound …

Speech emotion recognition is the crucial stream in emotional computing and also create
few issues owing to its complication in processing. The efficiency of the acoustic methods …

Conventional Speech Emotion Recognition (SER) approaches put more emphasis on
extracting magnitude spectrum-based features, such as Mel Frequency Cepstral Coefficients …