An evolutionary optimization method for selecting features for speech emotion recognition

نویسندگان

چکیده

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

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ژورنال

عنوان ژورنال: TELKOMNIKA Telecommunication Computing Electronics and Control

سال: 2023

ISSN: ['1693-6930', '2302-9293']

DOI: https://doi.org/10.12928/telkomnika.v21i1.24261