# A Semi-Supervised Lie Detection Algorithm Based on Integrating Multiple Speech Emotional Features
## 1 Introduction  
The rapid development of the Internet and social media has led to an information overload problem, including the spread of lies and misinformation. These phenomena can trigger significant negative consequences such as social unrest, political interference, and economic losses. Therefore, the development of lie detection technology is of great importance for building a more honest and secure social environment. Relevant research has already found wide application in critical fields such as law, the military, and forensics [1]. The psychological state of lying is a gradual and repetitive dynamic process. When people lie, they tend to become nervous, causing subtle changes in the vocal tract structure, with the fundamental frequency of the voice being higher than when telling the truth [2,3]. In the field of speech lie detection research, many acoustic features originally designed for speech emotion recognition have been confirmed to be equally crucial in identifying lies. These features can capture the tension, anxiety, or guilt that lians experience due to emotional fluctuations, providing key clues for lie detection by analyzing the pitch, rhythm, and intensity of speech. However, relying solely on these emotion-based acoustic features has limitations in revealing the complex psychological and physiological states of lians. To gain a deeper insight into deceptive behavior, it is essential to explore a broader set of features closely related to the act of lying, thereby enhancing the accuracy and depth of lie detection. There are some acoustic features in speech associated with lying [4]. These acoustic parameters change with varying stress levels, such as formants, Bark energy features, and Mel-frequency cepstral coefficients (MFCCs) [5,6]. Compared to the truth, lies exhibit fewer details, repetitive phrases, more content, shorter expression lengths,and inconsistencies in speech. Spectrum-based features can reflect the relationship between vocal tract shape and speech behavior [7]. [PERSON] et al. [8] achieved an accuracy of up to 81% in lie detection using Mel-frequency cepstral features in \"Real life trial data\" dataset. [PERSON] et al. [9] extracted Mel-frequency cepstral features and pitch frequency, achieving good performance in lie detection. With the rise of deep learning, researchers have also started applying it to lie detection. [PERSON] et al. [10] proposed a deep belief network based on K-means and singular value decomposition (K-SVD) for extracting deep features, showing that deep features can better represent lies compared to traditional acoustic features. [PERSON] et al. [11] used Bark spectrum energy features and neural network technology to detect subtle differences between lies and the truth. Although the aforementioned scholars have made significant achievements in the field of lie detection, data-driven deep neural networks heavily rely on large-scale labeled high-quality speech data. The lack of labeled data has become a critical issue constraining the development of the speech lie detection field. Semi-supervised learning algorithms provide a promising approach for speech lie detection tasks. [PERSON] et al. [12] proposed a mean-teacher model method, which optimizes semi-supervised learning by averaging model weights, simplifying the learning process of large datasets and improving test accuracy. [PERSON] et al. [13] respectively trained the bidirectional long short-term memory networks (BILSTM) and support vector machine (SVM) model, and adopted the decision-level score fusion scheme to further integrate the classification results of each model. [PERSON] et al. [14] proposed a multi-feature fusion speech lie detection algorithm based on attention mechanisms, constructing a hybrid model training network composed of a semi-supervised Denoising Auto Encoder (DAE) and a fully supervised Long Short-Term Memory (LSTM) network, effectively improving the accuracy of semi-supervised speech lie detection.