TELKOMNIKA Telecommunication Computing Electronics and Control
An evolutionary optimization method for selecting features for speech emotion recognition

TELKOMNIKA Telecommunication Computing Electronics and Control
An evolutionary optimization method for selecting features for speech emotion recognition

Nov 15, 2022

Human-computer interactions benefit greatly from emotion recognition from
speech. To promote a contact-free environment in this coronavirus disease
2019 (COVID’19) pandemic situation, most digitally based systems used
speech-based devices. Consequently, this emotion detection from speech has
many beneficial applications for pathology. The vast majority of speech
emotion recognition (SER) systems are designed based on machine learning
or deep learning models. Therefore, need greater computing power and
requirements. This issue was addressed by developing traditional algorithms
for feature selection. Recent research has shown that nature-inspired or
evolutionary algorithms such as equilibrium optimization (EO) and cuckoo
search (CS) based meta-heuristic approaches are superior to the traditional
feature selection (FS) models in terms of recognition performance.
The purpose of this study is to investigate the impact of feature selection
meta-heuristic approaches on emotion recognition from speech. To achieve
this, we selected the rayerson audio-visual database of emotional speech and
song (RAVDESS) database and obtained maximum recognition accuracy of
89.64% using the EO algorithm and 92.71% using the CS algorithm. For this
final step, we plotted the associated precision and F1 score for each of the
emotional classes.

Kesava Rao Bagadi, Chandra Mohan Reddy Sivappagari

http://telkomnika.uad.ac.id

Nov 15, 2022

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