Speech Emotion Recognition Method Based on Support Vector Machine and Suprasegmental Acoustic Features
https://doi.org/10.35596/1729-7648-2024-22-3-93-100
Abstract
The problem of recognizing emotions in a speech signal using mel-frequency cepstral coefficients using a classifier based on the support vector machine has been studied. The RAVDESS data set was used in the experiments. A model is proposed that uses a 306-component suprasegmental feature vector as input to a support vector machine classifier. Model quality was assessed using unweighted average recall (UAR). The use of linear, polynomial and radial basis functions as a kernel in a classifier based on the support vector machine is considered. The use of different signal analysis frame sizes (from 23 to 341 ms) at the stage of extracting mel-frequency cepstral coefficients was investigated. The research results revealed significant accuracy of the resulting model (UAR = 48 %). The proposed approach shows potential for applications such as voice assistants, virtual agents, and mental health diagnostics.
About the Authors
D. V. Krasnoproshin
Belarusian State University of Informatics and Radioelectronics
Belarus
Belarus
Master’s Student at the Department of Electronic Computing Facilities
220013, Minsk, P. Brovki St., 6
M. I. Vashkevich
Belarusian State University of Informatics and Radioelectronics
Belarus
Belarus
Vashkevich Maxim Iosifovich, Dr. of Sci. (Tech.), Professor at the Department of Electronic Computing Facilities
220013, Minsk, P. Brovki St., 6
Tel.: +375 17 293-84-78
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Review
For citations:
Krasnoproshin D.V., Vashkevich M.I. Speech Emotion Recognition Method Based on Support Vector Machine and Suprasegmental Acoustic Features. Doklady BGUIR. 2024;22(3):93-100. (In Russ.) https://doi.org/10.35596/1729-7648-2024-22-3-93-100
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