## 5 Results  
### Basic Experiment  
We first trained the three base models--Conformer, Unified Conformer, and U2++ Conformer--on the LibriSpeech train-960 dataset and conducted basic experiments using three traditional decoding methods during the decoding phase. After the training was completed, we applied arithmetic averaging for an initial model fusion. The models were then tested and validated on the dev_clean, dev_other, test_clean, and test_other datasets. The changes in loss values and learning rates during the training process are shown in Figures 8 and 9, respectively. The quantitative results from the testing and validation are presented in Table 3.  
Figure 8: Loss value variation curve. Figures 8 and 9 illustrate the changes in loss values and learning rates over 100 training epochs for the three models. Compared to the Conformer model, the Unified Conformer and U2++ Conformer models exhibit a more significant reduction in loss values and a smoother decline in learning rates. This improvement is attributed to the dynamic block training method and the enhancements from the causal convolution module, which have contributed to increased speech recognition accuracy. Additionally, although the U2++ Conformer shows a smooth decline in learning rates and the fastest reduction in loss values during the initial stages, its performance in later stages is not as strong as that of the Unified Conformer. This may be due to the increased model complexity introduced by the bidirectional decoding structure, which might have hindered the model's ability to fully learn effective features within the training epochs, ultimately affecting its final performance. Table 4 compares the recognition performance of three different network structures--Conformer, Unified Conformer, and U2++ Conformer--using three traditional decoding algorithms across two types of test and validation sets (clean and noisy environments). First, in terms of model methodology, Unified Conformer and U2++ Conformer show significant improvements in accuracy compared to the Conformer model across all dataset types, with WER decreasing by approximately 35% on a relative basis. However, when the environment shifts from clean to noisy, the WER for all models increases by about 125% relatively. This indicates that all three models still have limitations in handling noise, suggesting the need for further optimization to improve performance in complex environments. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Decoding Algorithm**} & \multicolumn{4}{c}{**WER(\%)**} \\ \cline{3-6}  & & **Dev-Clean** & **Dev-Other** & **Test-Clean** & **Test-Other** \\ \hline \multirow{3}{*}{Conformer (Arithmetic mean)} & attention & 7.10\% & 16.30\% & 7.23\% & 16.75\% \\  & ctc\_greedy\_search & 8.83\% & 18.46\% & 9.19\% & 18.77\% \\  & ctc\_prefix\_beam\_search & 8.70\% & 18.39\% & 9.06\% & 18.73\% \\ \hline \multirow{3}{*}{Unified\_Conformer (Arithmetic mean)} & attention & 4.55\% & 10.12\% & 4.93\% & 10.31\% \\  & ctc\_greedy\_search & 4.68\% & 12.05\% & 4.88\% & 12.16\% \\  & ctc\_prefix\_beam\_search & 4.63\% & 12.03\% & 4.84\% & 12.09\% \\ \hline \multirow{3}{*}{U2++\_Conformer (Arithmetic mean)} & attention & 4.41\% & 10.59\% & 4.87\% & 10.83\% \\  & ctc\_greedy\_search & 5.08\% & 12.38\% & 5.26\% & 12.59\% \\ \cline{1-1}  & ctc\_prefix\_beam\_search & 5.03\% & 12.34\% & 5.20\% & 12.48\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Basic test results. Figure 9: Learning rate variation curve. Second, regarding decoding algorithms, the attention mechanism performs well on the clean environment validation set, with WERs of 7.10%, 4.55%, and 4.41% for Conformer, Unified Conformer, and U2++ Conformer, respectively. However, in noisy environments, the WERs increase to 16.30%, 10.12%, and 10.59%, respectively. Similarly, the recognition accuracy of both CTC greedy search and CTC Prefix Beam Search declines as the dataset environment transitions from clean to noisy.