# Noise Modeling to Build Training Sets for Robust Speech Enhancement
## 4 Experiments and Discussion
As a result, the SE network trained on the synthetic training set achieves a slightly different noise reduction effect for the real noisy speech. Nonetheless, these results are generally encouraging despite the slightly poorer performance at the lowest SNR. In the \"GAN-test\" experiment, where the SE model is trained on the real training set, the enhancement scores for the synthetic and real noisy speech test sets are almost the same. The largest difference in PESQ is only 0.06 for 5 dB. For the CSIG, CBAK, and COVL metrics, the average differences are 0.1, 0.05, and 0.09, respectively, while for the SNR, the average difference is only 0.11. These results confirm that the generated noise is similar to the real noise. Hence, from the first \"GAN-train\" and \"GAN-test\" experimental group, we conclude that the SE model trained on our synthesized training set presents a robust SE effect for real noisy speech and that the SE model trained on the real training set achieves a similar SE effect for synthesized noisy speech. This proves that NM-GAN learned effectively from the input noise samples. Overall, NM-GAN can produce similar noise distributions that diversify as real noise. (2) From Table 2, we observe that the SE model trained on the real training sets provides consistent noise reduction levels for the synthetic \"limited\" noisy speech. However, when the SE is trained on the \"limited\" training set, the enhancement scores for the real noisy speech test set are not ideal. Comparing the PESQ metrics, the enhancement scores are 0.46, 0.68, and 0.74 higher for \(-\)5 dB, 0 dB, and 5 dB, respectively. The biggest performance increase for the CSIG, COVL, and CBAK metrics is 1.28, 1.59, and 1.76, respectively. Moreover, for the SNR metric, the average enhancement score is 4.01 higher when the developed model is tested on the synthetic \"limited\" noisy speech than on real noisy recordings. These experimental results highlight that an SE model trained on a \"limited\" training set is not robust to real noisy speech samples. We suspect that when an SE is trained on the \"limited\" training set using limited noise samples, the network may remember the \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **Metric** & **PESQ** & **CSIG** & **CBAK** & **COVL** & **SNR** \\ \hline \multirow{4}{*}{\begin{tabular}{c} **Trained Model/SNR/Test on** \\ \end{tabular} } & \multirow{4}{*}{\(-\)5 dB} & \({}^{\text{``Limited\" Synthetic noisy}}\) & 1.47 & 2.93 & 2.88 & 3.06 & 5.01 \\ \cline{3-6}  & & Real noisy & 1.01 & 2.33 & 2.14 & 1.74 & 1.89 \\ \cline{2-6}  & & \begin{tabular}{c} \({}^{\text{``Limited\" Synthetic noisy}}\) \\ \end{tabular} & 2.92 & 3.37 & 3.41 & 3.00 & 9.83 \\ \cline{2-6}  & & Real noisy & 2.24 & 2.09 & 2.15 & 1.92 & 5.88 \\ \cline{2-6}  & & \begin{tabular}{c} \({}^{\text{``Limited\" Synthetic noisy}}\) \\ \end{tabular} & 2.75 & 3.82 & 3.90 & 3.81 & 11.12 \\ \cline{2-6}  & & Real noisy & 2.01 & 2.74 & 2.31 & 2.05 & 6.16 \\ \hline \multirow{4}{*}{\begin{tabular}{c} GAN-test \\ (SE trained on real training set) \\ \end{tabular} } & \multirow{4}{*}{\(-\)5 dB} & \({}^{\text{``Limited\" Synthetic noisy}}\) & 1.10 & 2.26 & 2.40 & 2.17 & 3.03 \\ \cline{2-6}  & & Real noisy & 1.04 & 2.32 & 2.41 & 2.25 & 3.18 \\ \cline{2-6}  & & \
\begin{tabular}{c} \({}^{\text{``Limited\" Synthetic noisy}}\) \\ \end{tabular} & 2.07 & 3.11 & 3.24 & 2.15 & 7.28 \\ \cline{2-6}  & & Real noisy & 2.21 & 3.04 & 3.17 & 2.21 & 7.40 \\ \cline{2-6}  & &
\begin{tabular}{c} \({}^{\text{``Limited\" Synthetic noisy}}\) \\ \end{tabular} & 2.02 & 3.10 & 3.26 & 3.40 & 7.93 \\ \cline{2-6}  & & Real noisy & 2.05 & 3.17 & 3.29 & 3.37 & 8.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluation results for the second group of “GAN-train” and “GAN-test” experiments. specific noise waveforms, and thus its performance is less satisfactory for noisy speech with inconsistent noise waveforms. (3) Comparing Tables 1 and 2 reveals that the SE model trained on our synthetic set affords a better effect on noise suppression for real situations.