## 4 Experiments
### WSJ, AMI, and SWBD
#### 4.2.2 LA CTC/Attention  
Before applying location-aware attention CTC/Attention, the \(attentionwindow\) length \(C\) should be determined first. In this section, considering the size of three data sets, we choose the smaller sets, WSJ and AMI, to find the proper \(C\). The results are listed in Table 2.  
For WSJ, LA CTC/Attention obtains overall the best CER and WER performances in both dev and test sets when \(attentionwindow\) size equals 5. For AMI-IHM, the LA CTC/Attention performs worse than baseline results. However, focusing only on the results of LA CTC/Attention, the best results are achieved in the case that \(attentionwindow\) size is 3. Moreover, with \(attentionwindow\) length increasing, the performances degrade rapidly and fall far behind the original hybrid CTC/Attention framework. We suppose the reason is that the speech duration of AMI is too short. Revisiting the AMI-IHM and WSJ data, we figure out that the average frames of WSJ and AMI training data are 782  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**WSJ**} & \multicolumn{2}{c}{**LM Weight = 1**} & \multicolumn{3}{c}{**LM Weight = 0**} \\ \cline{2-9}  & & **CER** & & **WER** & & **CER** & & **WER** \\ \cline{2-9}  & **dev** & **test** & **dev** & **test** & **dev** & **test** & **dev** & **test** \\ \hline Baseline & 3.9 & 2.4 & 8.6 & 5.4 & 7.5 & 5.7 & 22.2 & 17.7 \\ \(C=3\) & **3.8** & 2.4 & **8.4** & 5.3 & 7.8 & 5.9 & 22.8 & 17.9 \\ \(C=5\) & 3.9 & **2.4** & 8.6 & **5.1** & **7.5** & **5.5** & 22 & **16.7** \\ \(C=7\) & 3.9 & 2.6 & 8.6 & 5.6 & 7.3 & 5.4 & **21.5** & 16.7 \\ \(C=9\) & 3.9 & 2.5 & 8.6 & 5.5 & 7.7 & 6.0 & 22.6 & 18.2 \\ \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**AMI**} & \multicolumn{2}{c}{**LM Weight = 1**} & \multicolumn{3}{c}{**LM Weight = 0**} \\ \cline{2-9}  & & **CER** & & **WER** & & **CER** & & **WER** \\ \cline{2-9}  & **dev** & **test** & **dev** & **test** & **dev** & **test** & **dev** & **test** \\ \hline Baseline & **22.3** & 23.2 & **35.1** & 37.4 & **23.4** & **24.7** & **39.6** & **42.2** \\ \(C=3\) & 22.3 & 23.6 & 35.3 & 38.1 & 23.5 & 25.1 & 40.1 & 43.1 \\ \(C=5\) & 24.3 & **23.0** & 39.2 & **36.3** & 24.2 & 25.8 & 41.5 & 44.5 \\ \(C=7\) & 26.1 & 24.3 & 41.6 & 38.8 & 25.4 & 27.5 & 43.7 & 47.1 \\ \(C=9\) & 24.9 & 26.2 & 39.3 & 42.0 & 27.6 & 25.9 & 47.2 & 44.3 \\ \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**LM Weight = 1**} & \multicolumn{3}{c}{**LM Weight = 0**} \\ \cline{2-9}  & **CER** & & **WER** & & **CER** & & **WER** \\ \cline{2-9}  & **eval** & **rt03** & **eval** & **rt03** & **eval** & **rt03** & **eval** & **rt03** \\ \hline Baseline & 30.2 & 31.9 & 47.8 & 50.6 & 30.8 & 32.5 & 51.0 & 54.0 \\ \(C=5\) & **29.4** & **31.5** & **47.3** & **50.3** & **30.1** & **32.4** & **50.5** & **53.8** \\ \hline \hline \end{tabular}
\end{table}
Table 2: The performances of the LA CTC/Attention for WSJ, AMI, and SWBD with different \(attentionwindow\) lengths. Additionally, the baseline is the results of ESPnet.  
and 257, respectively. Moreover, the encoder network subsamples four times. Thus, the phenomenon that proper \(attentionwindow\) size in AMI is shorter than that in WSJ is reasonable.  
For SWBD, since the average speech duration is 432 frames, which is twice that in AMI, we choose 5 as the SWBD \(attentionwindow\) size. Compared with the baseline, LA CTC/Attention obtains 2.6% and 1.3% relative CER reduction and 1.0% and 0.6% relative WER reduction for eval and rt03 with RNNLM. When decoding without RNNLM, it also achieves 2.3% and 0.3% relative CER decrease and 1.0% and 0.4% relative WER decrease.