## 5 Experimental Result and Analysis
### Experimental Setup and Metrics  
**Dataset:** The speech dataset used in this study was published by the research group at Tsinghua University [34]. It consists of PCM-format voice samples, which serve as the source data. These samples are monophonic, sampled at 8000 Hz, with a quantization depth of 16 bits. Following prior studies [24, 25], each speech sample was truncated to a duration of 10 s. The dataset includes 1000 utterances each from Chinese male, Chinese female, English male, and English female speakers, resulting in a total of 4000 speech samples. To construct the cover speech dataset, all samples were encoded using the AMR codec at a bit rate of 12.2 kbps. **Steganography methods:** In addition, we constructed four steganographic voice datasets by inserting hidden messages simulated as random binary bit streams into each sample using various steganographic techniques. There are four steganography methods, including [PERSON]'s method [17], [PERSON]'s method \(\eta=1\)[18], [PERSON]'s method \(\eta=2\)[18], and [PERSON]'s method \(\eta=4\)[18]. **Training set and testing set:** The training set included 3000 cover samples chosen at random from the cover speech dataset. 30,000 steganographic samples were created using a specific steganographic approach [17, 18] with different embedding rates. The embedding rates varied from 10% to 100% in 10% increments, with 3000 steganographic samples generated for each rate. As a result, the training set for each steganographic dataset included 3000 cover samples and 30,000 steganographic samples. The testing set wascreated using the same method, using 1000 cover samples and 10,000 steganographic samples that had comparable embedding rate distributions to the training set. In both sets, steganographic samples were labeled as positive, whereas cover samples were designated as negative. **Comparison methods:** As described in Section 2, [PERSON] et al. [24] proposed a DST-based approach, known as DST, for recognizing AMR-based voice streams with unknown embedding rates. [PERSON] et al. [25] have presented three steganalysis methods. The first method is called SVM-MSDA, which trains the SVM classifier based on the mixed sample data augmentation. The second method is called XGBoost-MSDA, which trains XGBoost based on the mixed sample data augmentation. The third method is called Kmeans-XGBoost, which combines clustering and ensemble learning. **Training Setup:** The training and evaluation are performed on a GeForce GTX 3080 GPU equipped with 11 GB of memory. The model and algorithms were implemented using the PyTorch v2.2.2 framework. The Adam optimizer was adopted with a learning rate of 0.001, and cross-entropy loss was utilized for the classification task, while the mean squared error is used for the regression task during training. The number of training epochs is set to 50, and the batch size is fixed at 64. A summary of the hyperparameters used in our method is presented in Table 1.  
**Metrics:** Furthermore, we use three criteria to assess the effectiveness of the suggested method. The first statistic is accuracy (ACC), which indicates the ratio of correctly classified samples to the total number of samples, defined as follows:  
\[ACC=\frac{N_{TN}+N_{TP}}{N_{TN}+N_{TP}+N_{FN}+N_{FP}} \tag{33}\]  
where \(N_{TN}\) represents the number of cover samples correctly classified, \(N_{TP}\) denotes the number of steganographic samples correctly classified, \(N_{FP}\) refers to the number of steganographic samples misclassified as cover samples, and \(N_{FN}\) is the number of cover samples misclassified as steganographic samples. The second metric is the error rate, which quantifies the proportion of incorrect predictions made by the model over the total number of predictions. It is formally defined as follows:  
\[ErrorRate=1-Accuracy \tag{34}\]  
The third metric is the false positive rate (FPR), defined as follows:  
\[FPR=\frac{N_{FP}}{N_{TN}+N_{FP}} \tag{35}\]  
The fourth metric is the false negative rate (FNR), namely  
\[FNR=\frac{N_{FN}}{N_{FN}+N_{TP}} \tag{36}\]