Deception is the action of causing a person to believe something, which is known to be lying with the provision of evidence to support such false beliefs with certain intensions. Identification of the deceptive characteristics manually is a challenging problem for the researchers. Thus, an automatic deception detector is necessary to be developed in order to ensure higher accuracy. Accordingly, this paper proposes a novel deception detector method called Moth Monarch optimization-based Deep Belief Neural Network (MMO-DBN). The proposed MMO-DBN classifier undergoes the phases of feature extraction and classification. Initially, the input speech signals are pre-processed to remove the noise present in the signal and subjected to feature extraction to extract the significant features, such as Mel Frequency Cepstral Coefficients (MFCC), Spectral Kurtosis, Spectral Spread, Spectral Centroid, minimum blood pressure, maximum blood pressure, respiration rate, and Tonal Power Ratio. Then, these extracted features are subjected to classification using Deep Belief Neural Network (DBN), which is trained with the proposed Moth Monarch optimization (MMO) algorithm that is the integration of Monarch Butterfly Optimization (MBO) and Moth Search (MS) algorithm. The performance of the proposed MMO-DBN is analyzed using the metrics, namely accuracy, sensitivity, and specificity. The proposed method obtained the higher accuracy, sensitivity, and specificity of 0.984, 0.9836, and 0.9375, respectively that shows the superiority of the proposed MMO-DBN in deception detection.

欺骗是指使某人相信某些东西的行为，众所周知，这与提供证据以某种意图支持这种错误信念有关。手动识别欺骗性特征是研究人员面临的难题。因此，有必要开发一种自动欺骗检测器以确保更高的精度。因此，本文提出了一种新颖的欺骗检测器方法，称为基于Moth Monarch优化的深度信念神经网络（MMO-DBN）。提出的MMO-DBN分类器经历了特征提取和分类的阶段。最初，对输入语音信号进行预处理以去除信号中存在的噪声，并对其进行特征提取以提取重要特征，例如梅尔频率倒谱系数（MFCC），频谱峰度，频谱扩展，频谱质心，最小血压，最大血压，呼吸频率和音调功率比。然后，使用深度信念神经网络（DBN）对这些提取的特征进行分类，该深度神经网络使用建议的Moth Monarch优化（MMO）算法进行训练，该算法是Monarch Butterfly Optimization（MBO）和Moth Search（MS）算法的集成。拟议的MMO-DBN的性能使用指标（即准确性，敏感性和特异性）进行分析。所提出的方法分别获得了0.984、0.9836和0.9375的更高的准确性，灵敏度和特异性，这表明了所提出的MMO-DBN在欺骗检测中的优越性。和音调功率比。然后，使用深度信念神经网络（DBN）对这些提取的特征进行分类，该深度神经网络使用建议的Moth Monarch优化（MMO）算法进行训练，该算法是Monarch Butterfly Optimization（MBO）和Moth Search（MS）算法的集成。拟议的MMO-DBN的性能使用指标（即准确性，敏感性和特异性）进行分析。所提出的方法分别获得了0.984、0.9836和0.9375的更高的准确性，灵敏度和特异性，这表明了所提出的MMO-DBN在欺骗检测中的优越性。和音调功率比。然后，使用深度信念神经网络（DBN）对这些提取的特征进行分类，该深度神经网络使用建议的Moth Monarch优化（MMO）算法进行训练，该算法是Monarch Butterfly Optimization（MBO）和Moth Search（MS）算法的集成。拟议的MMO-DBN的性能使用指标（即准确性，敏感性和特异性）进行分析。所提出的方法分别获得了0.984、0.9836和0.9375的更高的准确性，灵敏度和特异性，这表明了所提出的MMO-DBN在欺骗检测中的优越性。使用建议的Moth Monarch优化（MMO）算法进行训练，该算法是Monarch Butterfly Optimization（MBO）和Moth Search（MS）算法的集成。拟议的MMO-DBN的性能使用指标（即准确性，敏感性和特异性）进行分析。所提出的方法分别获得了0.984、0.9836和0.9375的更高的准确度，灵敏度和特异性，这表明了所提出的MMO-DBN在欺骗检测中的优越性。使用建议的Moth Monarch优化（MMO）算法进行训练，该算法是Monarch Butterfly Optimization（MBO）和Moth Search（MS）算法的集成。拟议的MMO-DBN的性能使用指标（即准确性，敏感性和特异性）进行分析。所提出的方法分别获得了0.984、0.9836和0.9375的更高的准确度，灵敏度和特异性，这表明了所提出的MMO-DBN在欺骗检测中的优越性。