## 4 Experimental Setup and Analysis of Results
### Ablation Experiment  
In this paper, we compare the WERs of three models--sparse adaptive fusion (green square dotted line), sparse normal fusion (orange circular dotted line), and adaptive fusion (blue triangular dotted line)--under various SNRs. The comparison results are presented in Figure 8.  
In the absence of noise, the WERs of all three models are relatively low. The sparse adaptive fusion model performs consistently well across all SNR conditions, achieving the lowest WER on average. Notably, it maintains a relatively low WER, even in high-noise conditions (0 dB), and exhibits minimal changes in mild (10 dB) and moderate (5 dB) noise conditions, demonstrating strong adaptability to noise. The WER of the sparse normal fusion model is approximately 8% in the clean condition, which is the highest among the three models, indicating average performance under ideal noiseless conditions. However, under high noise (0 dB), its WER rises to approximately 22%, approaching the performance of the sparse adaptive fusion model and demonstrating robust performance in extreme-noise environments. This comparison further highlights the noise resilience of models that incorporate sparse attention mechanisms. The adaptive fusion model performs well in noiseless and mildly noisy conditions, with WERs only slightly higher than those of the sparse adaptive fusion model. However, \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Transcription** & **WER/\%** \\ \hline GT & WELL INTO NOVEMBER & \\ A & WELLINGTON NOVEMBER & 33 \\ V & WHILE IS THE NOVUMBER & 167 \\ AV & WELL INTO NOVEMBER & 0 \\ \hline GT & WHEN THERE ISN’T MUCH EMSE IN THE GARDEN & \\ A & WHEN THERE ISN’T MUCH EMSE IN THE GARDEN & 0 \\ V & WHETHER IT’S M CHOUTER IN THE GARDEN & 83 \\ AV & WHEN THERE ISN’T MUCH EMSE IN THE GARDEN & 0 \\ \hline GT & SORT OF SECOND HALF OF OCTOBER & \\ A & SECOND HALF OF OCTOBER & 33 \\ V & SO YOU SEN O FROSOME & 100 \\ AV & SORT OF SECOND HALF OF OCTOBER & 0 \\ \hline GT & SOMETHING LIKE A GARDEN COMPOST & \\ A & SOMETHING LIKE A GARDEN COMPOST & 0 \\ V & SEMET LIKE A GARDEN COMPONS & 40 \\ AV & SOMETHING LIKE A GARDEN COMPOST & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Some examples of transcription results and word error rates (WER) for the proposed model in different modalities (GT: original text, A: audio-only, V: visual-only, AV: audio–visual). under high-noise (0 dB) conditions, the WER sharply increases to approximately 30%, the highest among the three models, indicating poor performance in extreme-noise environments. While the model is effective in typical noise conditions, its adaptability to high-noise levels requires further optimization. In conclusion, based on the experimental results under different SNR conditions, the sparse adaptive fusion model demonstrates strong performance in noiseless and mildly noisy environments and maintains good robustness in high-noise conditions, making it the best overall performer among the three models. In this study, we also conducted experiments on the model with audio and video frames out of sync, as illustrated in Figure 9. As the frame shift widens, the WERs increase. Among the models, the one without the adaptive attention fusion mechanism (orange circular dotted line) performs poorly, particularly exhibiting high sensitivity to temporal synchronization errors in the negative direction. This finding indicates that sparse normal fusion heavily depends on audio-video synchronization when handling AVSR tasks. Although its performance is relatively better at a frame shift of 0, its WER remains higher than that of the other two models. The sparse adaptive fusion model (green square dotted line) maintains a low WER across a range of frame shifts, suggesting that adaptive fusion is more effective in managing time synchronization errors. Specifically, this model demonstrates robustness, maintaining relatively low WERs for small frame shifts (\(-2\) to \(+2\)).