## 3 Experiments and Analysis
Therefore, we applied this architecture to the speaker recognition during the experiment to ensure that our proposed BLSTM-MFCCAE-TL method can utilize AE features and achieve better accuracy in speaker recognition. From the changes in the accuracy of the BLSTM-MFCC-TL and BLSTM-MFCCAE-TL methods, it was found that the accuracy of the validation set could be effectively improved by 1.9% by adding the input of additional AE features. Table 7 lists the computation time of each method for the proposed LSTM-MFCC-TL, BLSTM-MFCC-TL, and GMM-HMM-MFCCAE methods. The computation time was compared to understand the operational efficiency of an actual forecast. From the perspective of the first computation time and average computation time, it can be observed that the three methods proposed in this paper are faster than the traditional GMM-HMM-MFCCAE method. This is because the GMM-HMM model used in the GMM-HMM-MFCCAE method requires higher computation time, along with the computation time for processing the AE features. Therefore, the computation time of our proposed methods outperforms that of the GMM-HMM-MFCCAE method. Among the proposed methods, the proposed BLSTM-MFCCAE-TL method achieves the best performance in terms of learning effectiveness and accuracy. However, due to the additional time required for processing the AE features, the BLSTM-MFCCAE-TL method has a higher computation time compared to both the BLSTM-MFCC-TL method and the LSTM-MFCC-TL method. Nevertheless, the computation time of the BLSTM-MFCCAE-TL method is still superior to the computation time of the GMM-HMM-MFCCAE method. Finally, we attempted to perform additional training epochs using the proposed LSTM-MFCC-TL, BLSTM-MFCC-TL, and BLSTM-MFCCAE-TL methods. The speaker recognition accuracies at 20, 40, 60, and 80 epochs are listed in Table 8. \begin{table}
\begin{tabular}{c c c} \hline
**Method Name** & **First Computation Time (s)** & **Average Computation Time (s)** \\ \hline GMM-HMM-MFCCAE & 1.5195 & 1.5286 \\ LSTM-MFCC-TL & 0.6045 & 0.2698 \\ BLSTM-MFCC-TL & 0.5844 & 0.3856 \\ BLSTM-MFCCAE-TL & 0.9677 & 0.4052 \\ \hline \end{tabular}
\end{table} 
Table 7: Computation time comparison table of each method. We observed that the speaker recognition accuracy of the BLSTM-MFCCAE-TL method with extra AE features increased significantly during the change from epoch 20 to 40. However, the LSTM-MFCC-TL and BLSTM-MFCC-TL methods, without adding AE features, show only a slight increase in speaker recognition accuracy. Then, from the 40 th to the 60 th epoch, the speaker recognition accuracy of the LSTM-MFCC-TL method without AE features and the BLSTM-MFCC-TL method improved significantly. The BLSTM-MFCCAE-TL method, with additional AE features, exhibited only a minute increase in accuracy. This is because the BLSTM-MFCCAE-TL method has many input features. The accuracy was significantly improved in the previous training epoch, resulting in a less significant increase in speaker recognition accuracy during subsequent training. Finally, from the 60 th to the 80 th epoch, there was no evident increase in the method's performance, with or without adding AE features. Among them, the BLSTM-MFCCAE-TL method achieved a 95.03% validation set accuracy after the 80 th epoch training, which is the highest validation set accuracy rate in this experiment.