# Design of a Dual-Path Speech Enhancement Model
## 5 Results and Discussion  
### Ablation Test  
We conducted an ablation test to evaluate the effectiveness of the proposed dual-path SE model. The results, summarized in Table 1 and Figure 3, compare four model configurations: a single-path masking-based model (M), a single-path mapping-based model (S), a single-path two-branch model (M + S) with a shared encoder and separate decoders, and the final dual-path SE model (Proposed) that integrates M and M + S.  
In the case of M + S in Table 1, the pre-trained weights of model S are used as initial values in Encoder 1, Bottleneck 1, and Decoder 2. These weights are then fine-tuned during the retraining of the entire network. The decision to load the pre-trained weights was based on experimental results. More detailed implementation information, including the code and demo samples, can be found at [[https://github.com/seorim0/Dual_Path_Speech_Enhancement_Model](https://github.com/seorim0/Dual_Path_Speech_Enhancement_Model)]([https://github.com/seorim0/Dual_Path_Speech_Enhancement_Model](https://github.com/seorim0/Dual_Path_Speech_Enhancement_Model)) (accessed on 5 May 2025). Table 1 shows that the M + S model improves WB-PESQ and COVL, enhancing the overall speech quality. However, its lower CBAK and SNRseg scores indicate weakened background noise suppression and segmental SNR preservation. To analyze this trade-off, we examined the three key components contributing to CSIG, CBAK, and COVL: weighted spectral slope (WSS), log-likelihood ratio (LLR), and frequency-weighted segmental SNR (fwSNRseg). Unlike SNRseg, which measures frame-wise SNR uniformly across all frequency bands, fwSNRseg places more emphasis on the perceptually important frequencies, making it more aligned with human auditory perception. The results are illustrated in Figure 3.  
As shown in Figure 3, the M + S model, which uses a two-branch decoder and is trained jointly, achieves a higher fwSNRseg and significantly better WSS score than M and S, which use one-branch decoders each. This indicates improved noise suppression in perceptually important bands and better formant location preservation [31]. However, M + S yields a worse LLR score than any of the LLR scores of M or S, suggesting an increased spectral distortion, particularly in formant amplitude and spectral tilt [31]. These are unexpected results, and it (i.e., worse LLR score) explains that the joint training (M + S) could not make a noticeable improvement in terms of CSIG, CBAK, STOI, and SNRseg compared with the one-branch models. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Model** & **WB-PESQ** & **CSIG** & **CBAK** & **COVL** & **STOI (\%)** & **SNRseg** & **WER (\%)** \\ \hline Noisy & 1.97 & 3.35 & 2.44 & 2.63 & 91 & 1.69 & 5.08 \\ \hline M & 3.22 & 4.41 & 3.69 & 3.85 & 95 & 10.38 & 2.82 \\ S & 3.19 & 4.39 & 3.64 & 3.82 & 95 & 9.79 & 3.09 \\ \hline M + S & 3.29 \(\uparrow\) & 4.41 & 3.68 \(\downarrow\) & 3.89 \(\uparrow\) & 95 & 9.66 \(\downarrow\) & 2.61 \(\uparrow\) \\ \hline Proposed & **3.33 \(\uparrow\)** & **4.48 \(\uparrow\)** & **3.75 \(\uparrow\)** & **3.95 \(\uparrow\)** & **96 \(\uparrow\)** & **10.44 \(\uparrow\)** & **2.55 \(\uparrow\)** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Ablation test for dual-path SE model design. M refers to the case where only the masking branch is used, and S refers to the case where only the mapping branch is used. M + S is the case when a masking branch and a mapping branch are combined. Proposed is our dual-path SE model. Improvements over M and S are indicated with a red upward arrow (\(\uparrow\)), while decreases in performance are marked with a blue downward arrow (\(\downarrow\)). If there is no change in performance, no arrow is displayed. The best score is in bold. Figure 3: Performance comparison to verify the effectiveness of the models in terms of WSS, LLR, and fwSNRseg. Lower values for WSS and LLR scores are better (\(\downarrow\)), while higher values for fwSNRseg are better (\(\uparrow\)).