# SGM-EMA: Speech Enhancement Method Score-Based Diffusion Model and EMA Mechanism
## 2 Methodology
### Numerical SDE Solver
For all evaluation metrics, higher values indicate better performance. The results demonstrate the clear advantage of SGM-EMA in speech enhancement, as evidenced by the visual comparison. overall performance declined compared to the matched condition results in Table 4. This drop is primarily attributed to the fact that the specific characteristics of the test set were not exposed during training. The signal characteristic differences between the VB-DMD training data and the TIMIT-TUT test set were significant enough to cause a degradation in the evaluation scores. Nevertheless, under the mismatched conditions, our SGM-EMA model outperformed all other baseline methods across every evaluation metric, demonstrating exceptional robustness to variations in signal characteristics. Although the scores on the TIMIT-TUT test set were lower than those on the VB-DMD test set, SGM-EMA still outperformed the competing methods. This enabled SGM-EMA to achieve the best overall scores on the TIMIT-TUT dataset, highlighting its robustness to unseen noise characteristics and indicating its strong generalization ability. To further validate the statistical reliability of these results, we also report the 95% confidence intervals for each evaluation metric, as shown in the parentheses in Table 7. SGM-EMA consistently achieved the best average scores across all evaluation metrics under the mismatched conditions. While some confidence intervals partially overlap, the performance trend suggests an improvement compared to the baseline methods, especially in the SI-SDR and ESTOI. ### Trade-Offs in Model Design

Our model utilizes a deep U-Net with multiple resolution levels, a progressive growth path, and attention modules. While this complexity enables high-quality reconstruction and better modeling of global dependencies, it also leads to increased computational costs and slower inference times. We employed a score-based generative objective to encourage the generation of natural, manifold-aligned speech. This approach typically generalizes better to unseen noise types, as demonstrated by the results under mismatched conditions in Table 7. However, this comes at the cost of waveform fidelity, occasionally leading to the generation of artifacts under extreme noise conditions. The introduction of attention modules at specific resolutions improves global dependency modeling but increases the number of model parameters and inference complexity. The choice of group norm over alternatives like batch norm is due to its robustness to small batch sizes, which is critical for high-resolution spectrogram modeling. However, compared to the more lightweight batch norm, it introduces a small amount of additional computational overhead. ## 6 Conclusions

We propose a novel speech enhancement method that integrates an EMA mechanism with a U-Net architecture, trained and optimized as a score-based diffusion model. The experimental results show that under the conditions of matching training and testing, our method achieved better performance than other generative models and outperformed other comparative methods in both the ESTOI and SI-SDR evaluation indicators, demonstrating its excellent speech enhancement capability. Under mismatched conditions, while exhibiting some performance degradation compared to the matched scenario, our approach still outperformed all competing methods across all evaluation metrics, demonstrating remarkable generalization ability. Ablation studies further confirmed the importance of the reverse diffusion predictor and the EMA mechanism, revealing their critical roles in improving both enhancement quality and model stability. For future research, we plan to explore multimodal information fusion to further enhance speech enhancement performance. For instance, we will investigate incorporating visual information (such as speaker lip movement features) to combine the complementary advantages of audio and visual modalities, enabling the model to capture more comprehensive speech cues and thereby achieving better enhancement results.