## 4 Results and Comparison
### PNR-TDNN Experiments
#### 4.2.6 Discussion of Other Public Datasets  
The PNR-TDNNs are also evaluated in the well-known dataset of VoxCeleb1 [62] and cn-celeb_v2 [63]. Notably, VoxCeleb1 and cn-celeb_v2 contain a large amount of noise. To match the initial conditions with the zhvoice, a total of 300 noise-free and reverberation-free speakers are meticulously selected from VoxCeleb1, amounting to 100,482 utterances for pre-training (denoted as VoxCeleb1-S). Similarly, 122 noise-free and reverberation-free speakers are chosen from cn-celeb_v2, resulting in a total of 54,272 utterances for pre-training (denoted as cn-celeb_v2-S).  
To assess the performance of the PNR-TDNNs, two subsets of VoxCeleb1-S are randomly selected and labeled. As described in Table 10, these two subsets are named VoxCeleb1-A and VoxCeleb1-B, respectively. The same processing steps described above are also applied to the cn-celeb_v2-S. Table 11 shows the evaluation results in VoxCeleb1-A/B and cn-celeb_v2-A/B. As shown in Figure 14, we also present the best EER comparison plots of the four baseline models with the proposed T-Hu+EC+FAM on dataset VoxCeleb1-B and dataset cn-celeb_v2-B.The performance of CAM++'s EER on VoxCeleb1 and cn-celeb_v2 is erratic, which may be attributed to the more complex acoustic environments encountered in these datasets. Several new approaches, including MFA-Conformer, yield suboptimal EER compared to the ECAPA-TDNN.  
In evaluation experiments with VoxCeleb1 with only strong noise interference, small PNR-TDNNs presented optimal EER, e.g., T-Hu + EC + FAM achieved a 32% improvement in EER relative to ECAPA-TDNN. However, in the strong reverberation and noise situation, reverberation seriously interfered with speaker identification accuracy, although the PNR-TDNNs still had the lowest EER relative to numerous baselines. The effectiveness of the proposed method in the English dataset is indicated by these results. As for cn-celeb_v2 s2, the proposed method continued to yield the best recognition performance, albeit with a relatively smaller improvement. This can be attributed to the inherently stronger noise present in the original cn-celeb_v2, which negatively impacts the feature extraction capabilities of the backend network.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methodology**} & \multicolumn{2}{c}{**Dataset A**} & \multicolumn{2}{c}{**Dataset B**} & \multicolumn{2}{c}{**Dataset C**} \\  & **EER (\%)** & **MinDCF** & **EER (\%)** & **MinDCF** & **EER (\%)** & **MinDCF** \\ \hline EC & 8.72 & 0.201 & 9.59 & 0.188 & 6.44 & 0.157 \\ CAM++ & 7.43 & 0.196 & 8.62 & 0.192 & 17.22 & 0.334 \\ MFA-Conformer & 9.25 & 0.203 & 9.32 & 0.188 & 6.99 & 0.161 \\ Wepeaker & 12.43 & 0.208 & 12.04 & 0.195 & 10.26 & 0.174 \\ RedimNet & 7.97 & 0.206 & 8.39 & 0.187 & 6.53 & 0.160 \\ Gemini & 7.77 & 0.202 & 8.51 & 0.191 & 8.86 & 0.166 \\ \hline Hu + EC & 6.65 & 0.197 & 8.57 & 0.191 & 6.88 & 0.159 \\
**Hu + EC + FAM** & **6.49** & 0.196 & **7.95** & **0.185** & **6.13** & **0.154** \\
**S-Hu + EC + FAM** & 6.73 & **0.195** & 8.48 & 0.189 & 6.36 & 0.162 \\ T-Hu + EC + FAM & 7.20 & 0.202 & 8.62 & 0.191 & 7.02 & 0.163 \\ \hline \hline \end{tabular}
\end{table}
Table 9: PNR-TDNN’s best EER and MinDCF in a wide SNR range (\(-5\)\(\sim\)3 dB) (Note:EC = ECAPA-TDNN, Hu = HuBERT, S-Hu = small HuBERT, T-Hu = tiny HuBERT, FAM = fusion Res2 net + SE-attentive mean + multi-head attentive statistics pooling).