## 3 Experiments and Analysis
At the 20 th epoch, the LSTM-MFCC-TL method proposed in this paper has a loss value of 2.98% for the training set and 1.15% for the validation set. The loss value of the BLSTM-MFCC-TL method was 1.19% for the training set and 0.42% for the validation set. The BLSTM-MFCCAE-TL method had a training set loss of 0.75% and a validation set loss of 0.49%. From the comparison of the loss values of each method, it can be seen that there is not much difference between the  
Figure 14: We propose comparing the top 20 epochs loss values of the three methods. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method Name** & **Train Loss Value** & **Validation Loss Value** \\ \hline LSTM-MFCC-TL & 2.98\% & 1.15\% \\ BLSTM-MFCC-TL & 1.19\% & 0.42\% \\ BLSTM-MFCCAE-TL & 0.75\% & 0.49\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: We present a comparison table of the 20 th epoch loss value of the three methods. loss values of the training and the validation sets, and our method also obtains a lower loss value. Thus, the three proposed methods are all effectively trained. We then compared the accuracy of our proposed LSTM-MFCC-TL, BLSTM-MFCC-TL, and BLSTM-MFCCAE-TL methods, as shown in Figure 15 and Table 6.  
Figure 15 shows the changing trends of the training and validation data accuracies in the proposed LSTM-MFCC-TL, BLSTM-MFCC-TL, and BLSTM-MFCCAE-TL methods in the first 20 epochs. First, the accuracy of each method exhibits noticeable amplitude changes in the second epoch, and then it increases steadily. The results for the 20 th epoch are listed in Table 6.  
In this speaker recognition experiment, the accuracy is defined as the ratio of correctly recognizing the speaker to all prediction results. In comparison, the evaluated validation accuracy for the proposed methods, namely LSTM-MFCC-TL, BLSTM-MFCC-TL, and BLSTM-MFCCAE-TL, is 89.07%, 91.18%, and 93.08%, respectively. Therefore, using the same BLSTM model and MFCC feature, the BLSTM-MFCCAE-TL method, which incorporates AE features, significantly improved the validation accuracy from 91.18% to 93.08% compared to the BLSTM-MFCC-TL method. The architecture of the GMM-HMM-MFCCAE method [16] was designed for application to word classification and achieved a word classification accuracy of 92.81%. However, the GMM-HMM-MFCCAE method is only 64.48% accurate in the validation set applied to identity person recognition, which is the worst performance, as shown in Table 6. Then, we propose three identification accuracy methods; the LSTM-MFCC-TL method's accuracy rate is 89.07%. The validation set accuracy of the BLSTM-MFCC-TL method is 91.18%. The validation set accuracy of the BLSTM-MFCCAE-TL method is 93.08%. From the changes in the accuracy of the LSTM-MFCC-TL \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method Name** & **Train Accuracy** & **Validation Accuracy** \\ \hline GMM-HMM-MFCCAE & 65.60\% & 64.48\% \\ LSTM-MFCC-TL & 91.49\% & 89.07\% \\ BLSTM-MFCC-TL & 93.68\% & 91.18\% \\ BLSTM-MFCCAE-TL & 96.01\% & 93.08\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: We propose a comparison table of the 20-epoch accuracy of the three methods and the method constructed in reference [16]. Figure 15: Comparison of the first 20-epoch accuracy of our three proposed methods. and BLSTM-MFCC-TL methods, it can be seen that the architecture using BLSTM as the main model can effectively improve the validation set accuracy by 2.11%. The accuracy of the validation set of our constructed GMM-HMM-MFCCAE method was only 64.48%, which is the worst performance, as shown in Table 6. This is because the architecture of the GMM-HMM-MFCCAE method was designed to be applied to the classification of words in the study of [16], and a classification accuracy of more than 90% of the words was obtained. The LSTM-MFCC-TL method, BLSTM-MFCC-TL method, BLSTM-MFCCAE-TL method, and GMM-HMM-MFCCAE method used in the experiment are all speaker recognition methods. Although the GMM-HMM-MFCCAE method was originally designed for text recognition, we took reference from its AE feature architecture.