## 3 Experiments and Analysis  
To investigate the performances of the methods in this paper, we execute speaker recognition experiments using the AISHELL-1 dataset [34]. The AISHELL-1 dataset consists of Mandarin speech recorded with a high-fidelity microphone (44.1 kHz, 16-bit). The AISHELL-1 dataset was produced by the audio recorded through the high-fidelity microphone, which was down-sampled to 16 kHz. The AISHELL-1 dataset is a public speech dataset with a total of 400 speakers from different accent regions in China who participated in the recording. In this paper, the AISHELL-1 dataset is used in the speaker recognition. In our experiments, we use the entire dataset of 400 speakers, divided into a training set and validation set with proportions of 90% and 10%, respectively. Table 4 lists the experimental parameters of the proposed LSTM-MFCC-TL, BLSTM-MFCC-TL, and BLSTM-MFCCAE-TL methods. The main models in Table 4 refer to the LSTM and BLSTM architectures in the three methods, where the input feature shape is the feature size of the main input model, and the output feature shape is the feature size output by the main model. Table 4 shows that the LSTM-MFCC-TL and BLSTM-MFCC-TL methods proposed in this paper are all MFCC feature vectors of \((512,\,39)\) in the main model's input part. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method Name** & **LSTM-MFCC-TL** & **BLSTM-MFCC-TL** & **BLSTM-MFCCAE-TL** \\ \hline Input features & MFCC & MFCC & MFCC and AE \\ Main model & LSTM & BLSTM & BLSTM \\ Learning rate & 0.00001 & 0.00001 & 0.00001 \\ Loss function & Triplet loss & Triplet loss & Triplet loss \\ Optimizer & Adam & Adam & Adam \\ Batch size & 64 & 64 & 64 \\ Epochs & 20 & 20 & 20 \\ Input feature shape & (512, 39) & (512, 39) & (538, 39) \\ Output feature shape & (1, 128) & (1, 128) & (1, 128) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental parameters of our three proposed methods. Figure 13: BLSTM model architecture diagram in the BLSTM-MFCCAE-TL method. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Layers** & **Size** & **Information** \\ \hline Input & (538, 39) & \\ BLSTM & (538, 256) & Units: 128 \\ Dropout & (538, 256) & Drop: 0.3 \\ Flatten & (1, 137,728) & \\ Dense (Output) & (1, 128) & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: BLSTM model parameter table in BLSTM-MFCCAE-TL method. The input of the main model of the BLSTM-MFCCAE-TL method uses a combination of MFCC and AE features, and its size is a feature vector of (538, 39). The main model of the LSTM-MFCC-TL method is the LSTM architecture, and the main model of the BLSTM-MFCC-TL and BLSTM-MFCCAE-TL methods is the BLSTM architecture. The learning rate used during training is 0.00001, and the optimizer is Adam. The batch size is 64. The loss function uses triplet loss. The number of training epochs is 20, and the output feature vector is of size (1, 128). Next, model training was carried out for the above model and the experimental parameters and methods. During training, the loss value and accuracy rate of each round were recorded to observe each model's training effect from the numerical changes. First, we compared the loss values of our proposed LSTM-MFCC-TL, BLSTM-MFCC-TL, and BLSTM-MFCCAE-TL methods, as shown in Figure 14 and Table 5.  
The six-line graph in Figure 14 shows the change trends of the training and the validation data loss values of the LSTM-MFCC-TL, the BLSTM-MFCC-TL, and the BLSTM-MFCCAE-TL methods in each epoch. In the second epoch, the loss value of each method was significantly reduced. Subsequently, the gap between each method's training and the validation set loss values becomes increasingly closer, and the training loss value gradually becomes stable. From Table 5, we can see a comparison of the loss values of the three proposed methods in the 20 th round, especially to observe whether the training and the validation set loss values between the methods exhibit overfitting.