A Novel Dual Kernel Support Vector-Based Levy Dung Beetle Algorithm for Accurate Speech Emotion Detection

Abstract

Human emotions are easy to identify through facial expressions, body movements, and gestures. Speech carries a lot of emotional cues including variations in pitch, tone, intensity, and rhythm. In recent years, the increasing demand for human–computer interaction has spurred the development of speech recognition methods. Traditional Speech emotion detection methods are less effective in recognizing emotions, considering features like pitch, intensity, and spectral characteristics. To address these issues, this paper proposed a novel method named Dual Kernel Support Vector based Levy Dung Beetle (DKSV-LDB) Algorithm to accurately identify emotions like happiness, anger, sadness, etc. from speech patterns. In this study, the model is designed by combining a Dual Kernel Support Vector Machine (SVM) method with a Dung beetle Optimization algorithm, enriched by the Levy Flight strategy. This work conducted experiments in the datasets namely the CREMA-D, TESS, and EMO-DB (German). The performance evaluation measures such as accuracy, precision, recall, F-measure, and specificity are utilized for the evaluation of the proposed DKSV-LDB method and these results are compared with existing methods. The DKSV-LDB method achieved accuracy, precision, recall, F-measure, and specificity of 98.57%, 97.91%, 97.86%, 97.84%, and 97.78%. The experimental results depict the performance of the developed DKSV-LDB technique for speech emotion identification.