# SGM-EMA: Speech Enhancement Method Score-Based Diffusion Model and EMA Mechanism
## 2 Methodology
### Numerical SDE Solver
Specifically, removing the reverse diffusion method led to decreases in the SI-SNR, ESTOI, and PESQ scores, highlighting the importance of accurately solving the reverse SDE to generate high-quality enhanced speech. Among these, the drop in ESTOI reflects a reduction in speech intelligibility, while decreases in the PESQ and SI-SNR indicate losses in perceptual quality and signal fidelity, respectively. Similarly, removing the EMA mechanism also caused performance degradation, confirming its critical role in ensuring model stability and convergence. In addition, the ESTOI scores consistently increased with the inclusion of each component, particularly the reverse diffusion method \begin{table}
\begin{tabular}{c c} \hline
**Name** & **Specific Configuration** \\ \hline Memory & 32 GB \\ OS Bit & 64-bit \\ Programming Language & CPython 3.12.3 \\ Dataset & VB-DMD and TIMIT-TUT \\ Deep Learning Framework & PyTorch Lightning 2.1.4 \\ \hline \end{tabular}
\end{table} 
Table 3: _Cont._and EMA. When both were applied together, the highest ESTOI score of 0.91 was achieved, indicating a substantial improvement in intelligibility. These findings confirm the necessity of incorporating both techniques into the proposed framework, as they not only improved signal quality and perceptual clarity but also ensured that the enhanced speech remained intelligible, an essential factor for real-world applications, such as voice communication and assistive technologies. ## 5 Experimental Results and Discussion

### Experimental Results

We used matched training and testing datasets--specifically, we trained the model on the VB-DMD training set and evaluated it on the corresponding test set; the experimental results are presented in Table 6. The average values of all metrics in Table 6 are visualized in Figure 5a. RVAE is an unsupervised speech enhancement method trained solely on clean speech (VB). The results show that although our SGM-EMA model did not outperform CMGAN in terms of the PESQ metric, this was largely due to CMGAN's specifically designed PESQ optimization module in its discriminator, as well as the use of a multi-domain joint loss function. These design choices effectively suppress distortion, allowing CMGAN to achieve higher PESQ scores. Nevertheless, SGM-EMA still outperformed other models and surpassed the discriminative model SERGAN, demonstrating strong speech restoration capabilities. Compared to other baseline methods, such as RVAE and CDiffuse, SGM-EMA achieved significant improvements in both the PESQ and SI-SDR metrics. To further validate the statistical reliability of the results, we report the 95% confidence intervals for each evaluation metric (values in parentheses in Table 6). Although the confidence intervals of SGM-EMA for PESQ and ESTOI metrics partially overlapped with those of baseline models, like CMGAN and SERGAN, our model demonstrated systematic superiority: it achieved higher mean values across all metrics while maintaining narrower confidence intervals, highlighting its exceptional speech recovery capability. Particularly for the SI-SDR metric, SGM-EMA not only sustained superior average performance but