## 4 Experimental Results
### Objective Quality Evaluation  
Table 1 shows the average PESQ results obtained by reference methods (DNN\(-\)PLC, WSOLA\(-\)PLC) and the proposed methods (DNN+GLA\(-\)PLC and DNN+WSOLA\(-\)PLC) under the packet loss rates of 5%, 10%, 15%, 20%, and 30%, respectively. From Table 1, it can be seen that, compared with non\(-\)PLC, all the methods can achieve the effect of PLC. The higher the packet loss rate, the more obvious the effect of PLC. When the packet loss rate is low, the PESQ scores of the WSOLA\(-\)PLC method and the DNN\(-\)PLC method are very close. When the packet loss rate gradually increases, the WSOLA\(-\)PLC method shows its robustness, and the PESQ score gradually exceeds that of the DNN\(-\)PLC method. As a contrast experiment, the DNN+GLA\(-\)PLC method uses the  
Figure 11: Waveform comparison: (**a**) 30% packet loss; (**b**) DNN\(-\)PLC; (**c**) DNN+GLA\(-\)PLC; (**d**) WSOLA\(-\)PLC; (**e**) DNN+WSOLA\(-\)PLC; (**f**) no packet loss. Figure 10: Waveform comparison: (**a**) 5% packet loss; (**b**) WSOLA\(-\)PLC; (**c**) DNN+WSOLA\(-\)PLC. GLA to iteratively modify the phase spectrum through the amplitude spectrum. Compared with the DNN\(-\)PLC method, the PESQ score of the DNN+GLA\(-\)PLC method is improved by about 0.2. In addition, as a method that can also correct the phase, the PESQ score of the DNN+GLA\(-\)PLC method is higher than that of the WSOLA\(-\)PLC method, because the amplitude distortion of the WSOLA\(-\)PLC method in the PLC will lead to the decline of speech quality, whereas the DNN+GLA\(-\)PLC method can obtain higher speech quality by using more accurate amplitude information. The rightmost column in Table 1 is the PESQ scores of the proposed PLC method based on DNN and WSOLA. It can be seen that, compared with all the methods, the proposed method can achieve the best results. Moreover, in the case of 30% packet loss rate, the PESQ score of the proposed method can be improved by about 0.9, which shows that the robustness of the proposed method is still guaranteed, even in the case of high packet loss rate. From Table 2, it can be seen that all reference methods and proposed methods can improve the intelligibility of packet loss speech. The improvement effect becomes more obvious with the increase of packet loss rate. For the DNN\(-\)PLC method, the STOI improvement is very close to that of the WSOLA\(-\)PLC method at each packet loss rate, but DNN\(-\)PLC is still slightly better than that of the WSOLA\(-\)PLC method, because the loss function of the DNN\(-\)PLC method in training is very close to the STOI criteria. In addition, it can be seen that the DNN+GLA\(-\)PLC method improves the intelligibility of the DNN\(-\)PLC method by introducing the GLA to correct phase, especially for high packet loss rate. Due to the combination of DNN and WSOLA, the DNN+WSOLA\(-\)PLC method achieves the highest STOI increments compared with other methods. Table 3 shows the average LSD results of reference methods (DNN\(-\)PLC and WSOLA\(-\)PLC) and the proposed methods (DNN+GLA\(-\)PLC and DNN+WSOLA\(-\)PLC) under the packet loss rates of 5%, 10%, 15%, 20%, and 30%, respectively. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Packet Loss Rate**} & \multicolumn{4}{c}{**PESQ**} \\ \cline{2-6}  & **Non\(-\)PLC** & **DNN\(-\)PLC** & **DNN+GLA\(-\)PLC** & **WSOLA\(-\)PLC** & **DNN+WSOLA\(-\)PLC** \\ \hline
5\% & 3.0467 & 3.2653 & 3.3384 & 3.2756 & **3.4360** \\
10\% & 2.4859 & 2.8190 & 2.9527 & 2.8907 & **3.0871** \\
15\% & 2.2267 & 2.5988 & 2.7486 & 2.6743 & **2.8968** \\
20\% & 1.9474 & 2.3665 & 2.5533 & 2.4809 & **2.7194** \\
30\% & 1.4832 & 1.9863 & 2.2349 & 2.1545 & **2.4217** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: PESQ results.