基于双核支持向量的新型利维蜣螂算法,用于准确的语音情感检测

IF 1.8 3区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
Tian Han, Zhu Zhang, Mingyuan Ren, Changchun Dong, Xiaolin Jiang
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引用次数: 0

摘要

人类的情绪很容易通过面部表情、肢体动作和手势来识别。语音则包含许多情绪线索,包括音调、语气、强度和节奏的变化。近年来,人机交互需求的不断增长推动了语音识别方法的发展。传统的语音情感检测方法考虑到音调、强度和频谱特性等特征,在识别情感方面效果较差。为解决这些问题,本文提出了一种名为基于莱维蜣螂算法(DKSV-LDB)的双核支持向量新方法,可从语音模式中准确识别喜怒哀乐等情绪。在这项研究中,模型的设计结合了双核支持向量机(SVM)方法和蜣螂优化算法,并使用了李维飞行策略。这项工作在 CREMA-D、TESS 和 EMO-DB(德语)数据集上进行了实验。采用准确度、精确度、召回率、F-measure 和特异性等性能评估指标对所提出的 DKSV-LDB 方法进行了评估,并将这些结果与现有方法进行了比较。DKSV-LDB 方法的准确度、精确度、召回率、F-measure 和特异性分别达到了 98.57%、97.91%、97.86%、97.84% 和 97.78%。实验结果表明了所开发的 DKSV-LDB 技术在语音情感识别方面的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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

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

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.

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来源期刊
Circuits, Systems and Signal Processing
Circuits, Systems and Signal Processing 工程技术-工程:电子与电气
CiteScore
4.80
自引率
13.00%
发文量
321
审稿时长
4.6 months
期刊介绍: Rapid developments in the analog and digital processing of signals for communication, control, and computer systems have made the theory of electrical circuits and signal processing a burgeoning area of research and design. The aim of Circuits, Systems, and Signal Processing (CSSP) is to help meet the needs of outlets for significant research papers and state-of-the-art review articles in the area. The scope of the journal is broad, ranging from mathematical foundations to practical engineering design. It encompasses, but is not limited to, such topics as linear and nonlinear networks, distributed circuits and systems, multi-dimensional signals and systems, analog filters and signal processing, digital filters and signal processing, statistical signal processing, multimedia, computer aided design, graph theory, neural systems, communication circuits and systems, and VLSI signal processing. The Editorial Board is international, and papers are welcome from throughout the world. The journal is devoted primarily to research papers, but survey, expository, and tutorial papers are also published. Circuits, Systems, and Signal Processing (CSSP) is published twelve times annually.
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