# Speech Inpainting Based on Multi-Layer Long Short-Term Memory Networks
## 5 Results and Discussion
### Inpainting Performance with Different Numbers of LSTM Layers  
To examine the inpainting performance of different numbers of LSTM layers on the different single-speaker datasets, we calculated two types of average MOSs for different gap lengths, as shown in Tables 7 and 8. Figure 3: Training losses of the proposed speech inpainting models on single-speaker datasets with different numbers of LSTM layers: (**a**–**e**) speech inpainting models with two to six LSTM layers, respectively; (**f**) the 5-layer LSTM model trained on four multi-speaker datasets. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Bandwidth**} & \multicolumn{2}{c}{**LSTM**} & \multicolumn{6}{c}{**Gap Length (ms)**} \\ \cline{3-10}  & & **Layers** & **20** & **40** & **50** & **100** & **200** & **500** & **1000** \\ \hline \multirow{4}{*}{LJSpeech} & \multirow{4}{*}{NB} & 2 & 4.17 & 3.96 & 3.92 & 3.84 & 3.71 & 3.27 & 2.69 \\  & & 3 & 4.15 & 3.91 & 3.88 & 3.80 & 3.66 & 3.17 & 2.56 \\  & & 4 & 4.14 & 3.95 & 3.92 & 3.79 & 3.67 & 3.21 & 2.59 \\  & & 5 & 4.16 & 3.96 & 3.92 & 3.83 & 3.67 & 3.19 & 2.57 \\  & & 6 & 4.10 & 3.91 & 3.86 & 3.77 & 3.62 & 3.13 & 2.49 \\ \hline \multirow{4}{*}{RyanSpeech} & \multirow{4}{*}{NB} & 2 & 4.08 & 3.94 & 3.89 & 3.74 & 3.48 & 3.02 & 2.52 \\  & & 3 & 4.15 & 4.00 & 3.94 & 3.74 & 3.53 & 3.07 & 2.56 \\  & & 4 & 4.11 & 3.96 & 3.91 & 3.73 & 3.52 & 3.03 & 2.54 \\  & & 5 & 4.07 & 3.91 & 3.85 & 3.68 & 3.43 & 2.92 & 2.44 \\  & & 6 & 4.13 & 3.97 & 3.90 & 3.73 & 3.48 & 3.01 & 2.48 \\ \hline \multirow{4}{*}{Libri\_26} & \multirow{4}{*}{NB} & 2 & 4.29 & 4.15 & 4.10 & 3.90 & 3.72 & 3.25 & 2.68 \\  & & 3 & 4.25 & 4.13 & 4.10 & 3.93 & 3.75 & 3.29 & 2.76 \\  & & 4 & 4.25 & 4.14 & 4.11 & 3.92 & 3.77 & 3.30 & 2.78 \\  & & 5 & 4.29 & 4.17 & 4.14 & 3.98 & 3.80 & 3.37 & 2.86 \\  & & 6 & 4.24 & 4.12 & 4.09 & 3.89 & 3.71 & 3.22 & 2.67 \\ \hline \multirow{4}{*}{Libri\_32} & \multirow{4}{*}{NB} & 2 & 4.26 & 4.09 & 4.03 & 3.92 & 3.66 & 3.21 & 2.75 \\  & & 3 & 4.29 & 4.07 & 4.02 & 3.88 & 3.64 & 3.17 & 2.69 \\  & & 4 & 4.24 & 4.06 & 4.01 & 3.84 & 3.63 & 3.19 & 2.70 \\  & & 5 & 4.24 & 4.06 & 4.00 & 3.85 & 3.61 & 3.14 & 2.66 \\  & & 6 & 4.25 & 4.10 & 4.04 & 3.91 & 3.68 & 3.21 & 2.74 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Average MOSs for different gap lengths and LSTM layers across four single-speaker datasets (NB). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Bandwidth**} & \multicolumn{2}{c}{**LSTM**} & \multicolumn{6}{c}{**Gap Length (ms)**} \\ \cline{3-10}  & & **Layers** & **20** & **40** & **50** & **100** & **200** & **500** & **1000** \\ \hline \multirow{4}{*}{LJSpeech} & \multirow{4}{*}{WB} & 2 & 4.08 & 3.89 & 3.88 & 3.77 & 3.55 & 3.00 & 2.29 \\  & & 3 & 4.02 & 3.86 & 3.82 & 3.70 & 3.47 & 2.89 & 2.21 \\  & & 4 & 4.03 & 3.85 & 3.81 & 3.69 & 3.44 & 2.89 & 2.21 \\  & & 5 & 4.13 & 3.94 & 3.89 & 3.74 & 3.5 It can be observed in Table 7 that the two-layer LSTM model performed well across all gap lengths on the LJSpeech dataset. Meanwhile, on the RyanSpeech dataset, the performance of the three-layer LSTM model was better compared to the other models. Notably, on the Libri_26 dataset, the five-layer LSTM model achieved the highest MOSs across all gap lengths and datasets. In the case of the Libri_32 dataset, the six-layer LSTM model exhibited marginally superior performance compared to the two-layer LSTM model, although the difference was not statistically significant. In Table 8, the results show that on the LJSpeech dataset, the two-layer LSTM model achieved higher average MOSs for gap lengths greater than or equal to 100 ms, whereas the five-layer LSTM model performed better when the gap length was less than 100 ms. Similar results can be observed for the RyanSpeech and Libri_26 datasets in Table 7. However, on the Libri_32 dataset, the three-layer LSTM model achieved the highest MOSs across all datasets.