## 5 Results
### Ablation Experiment  
We combined the two aforementioned optimizations, using the Mixformer hybrid structure model and the dynamic adaptive decoding algorithm for the experiment. The contents of the previous experiments were summarized and compared in an ablation study. The experimental results are presented in Table 11 and Figure 10.  
The above experimental results clearly demonstrate that each step of improvement significantly enhances recognition performance. First, the model fusion method (Mixformer) shows lower WER compared to individual models across both attention and other decoding \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} & attention & 7.00\% & 16.26\% & 7.18\% & 16.72\% \\  & ctc\_greedy\_search & 8.83\% & 18.41\% & 9.16\% & 18.74\% \\  & ctc\_prefix\_beam\_search & 8.69\% & 18.35\% & 9.06\% & 18.69\% \\ \hline \multirow{3}{*}{Unified\_Conformer} & attention & 4.56\% & 10.12\% & 4.93\% & 10.32\% \\  & ctc\_greedy\_search & 4.67\% & 12.05\% & 4.87\% & 12.17\% \\  & ctc\_prefix\_beam\_search & 4.63\% & 12.02\% & 4.83\% & 12.10\% \\ \hline \multirow{3}{*}{U2++\_Conformer} & attention & 4.39\% & 10.54\% & 4.81\% & 10.79\% \\  & ctc\_greedy\_search & 5.05\% & 12.37\% & 5.27\% & 12.60\% \\  & ctc\_prefix\_beam\_search & 5.01\% & 12.32\% & 5.20\% & 12.47\% \\ \hline \multirow{3}{*}{Mixformer} & attention & 4.60\% & 10.12\% & 4.91\% & 10.22\% \\  & ctc\_greedy\_search & 4.58\% & 11.85\% & 4.85\% & 12.06\% \\  & ctc\_prefix\_beam\_search & 4.54\% & 11.83\% & 4.83\% & 11.97\% \\ \hline \multicolumn{3}{c}{Conformer} & adaptive attention fusion rescoring & 5.17\% & 12.35\% & 5.43\% & 12.36\% \\ \hline Unified\_Conformer & adaptive attention fusion rescoring & 4.35\% & 10.02\% & 4.73\% & 10.13\% \\ \hline U2++\_Conformer & adaptive attention fusion rescoring & 4.14\% & 10.20\% & 4.42\% & 10.08\% \\ \hline Mixformer & adaptive attention fusion rescoring & 3.92\% & 8.94\% & 4.07\% & 9.31\% \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Summary of ablation experiment results. Figure 10: Summary of recognition results under different methods. algorithms, indicating its effectiveness in enhancing model robustness and generalization capabilities. Second, the dynamic adaptive decoding algorithm significantly reduces WER across various test environments compared to traditional decoding algorithms (attention, CTC greedy search, CTC prefix beam search), further improving recognition accuracy. Additionally, the ablation experiment confirms the effectiveness of each improvement step, highlighting the independent contributions of model fusion and decoding algorithm innovation to enhancing speech recognition performance, without mutual interference. Ultimately, the combination of the model fusion and dynamic adaptive decoding algorithm yielded the best recognition performance across all test environments, with WERs of 3.92%, 8.94%, 4.07%, and 9.31% in the Dev-Clean, Dev-Other, Test-Clean, and Test-Other environments, respectively, validating the effectiveness and practicality of the proposed methods. This innovative approach offers new insights for the development of speech recognition technology, demonstrating significant advantages in improving recognition accuracy and handling complex environments. Additionally, this study conducts a comprehensive comparison of the proposed Mixformer model with current state-of-the-art speech recognition models, as shown in Table 12 and Figure 11. The experimental results indicate that while DeepSpeech and Kaldi perform reasonably well on clean datasets such as Test-Clean, their performance significantly degrades in noisy environments, such as the Test-Other dataset, highlighting limitations in robustness.