# A Semi-Supervised Lie Detection Algorithm Based on Integrating Multiple Speech Emotional Features
## 3 Experimental Section
### Experimental Setup and Evaluation Metrics  
This study investigated the practical performance of model algorithms with different numbers of labels, specifically set at 200, 600, and 1200 labels. Firstly, in the data augmentation section, different noise coefficients were selected for different types of features. For the 312-dimensional acoustic statistical features, the strong augmentation noise coefficient \(\lambda_{\mathit{austrong}}\) was set to 1, and the weak augmentation noise coefficient \(\lambda_{\mathit{austweak}}\) was set to 0.32. For the 64-dimensional log Mel spectrogram features, the strong augmentation noise coefficient \(\lambda_{\mathit{austrong}}\) was set to 1, and the weak augmentation noise coefficient \(\lambda_{\mathit{sutweak}}\) was set to 0.35. All experiments were conducted on the Windows 11 operating system, utilizing an NVIDIA RTX 3060 graphics card (NVIDIA, Santa Clara, CA, USA). The experiments were performed using the PyTorch (Version: 2.2.1+cu118) deep learning framework and coded in the Python programming language. The Adam algorithm was employed for model optimization, with the number of iterations set to 100 epochs. In the model, the Dropout parameter was set to 0.9 to mitigate overfitting. Each experiment was repeated 10 times, and the average was computed to mitigate the influence of random errors. The WA (Weighted Accuracy) is selected as the primary evaluation metric for assessing the performance of the recognition model. In order to verify the rationality of the proposed model, three sets of ablation experiments were designed to analyze the improvement effect of the joint attention model and unsupervised fusion loss. The specific settings are as follows:1. [label=(0)]
2. MFDF: Directly concatenate and fuse the 312 dimensional acoustic statistical features and 64 dimensional logarithmic Mel spectrogram features trained by the model in one dimension for lie detection. 3. MFDF + ML: Directly concatenate and fuse the 312 dimensional acoustic statistical features and 64-dimensional logarithmic Mel spectrogram features after model training, and combine them with a semi-supervised learning algorithm improved by LMMD loss for lie detection. 4. JAF: Using joint attention method to fuse the 312 dimensional acoustic statistical features and 64 dimensional logarithmic Mel spectrogram features after model training for lie detection. As can be seen from Table 1, the performance of the proposed algorithm is better than other modules. This is due to the fusion of the two types of features by joint attention, which further captures the detailed information of the two types of features and improves the lie detection accuracy to a certain extent; by introducing the semi-supervised joint optimization loss, the potential feature information is deeply mined, which effectively improves the lie detection accuracy. It is worth noting that the accuracy of 600 labels is lower than that of 200 labels. As the number of labels increases, more low-quality or noisy labels will be introduced, causing the model to be disturbed during prediction, thereby reducing accuracy. However, as the number of labels further increased to 1200, the model had sufficient data and information to better learn and distinguish subtle differences between different labels, resulting in an improvement in accuracy. In Figure 3, we also show the accuracy and loss convergence curves of the algorithm in each group of experiments. To more clearly demonstrate the trend of changes, the number of iterations was set to 600. When the number of labels is 600, the experimental results indicate that the average accuracy of (d) is significantly higher than that of (a), (b), and (c), with the highest value reaching 63.3%. The highest values for (a) and (c) do not exceed 62%, and (b) performs the worst. Compared to (a) and (b), the loss for (c) and (d) has decreased. Overall, (d) demonstrates the best performance.