## 4 Experiments
### WSJ, AMI, and SWBD  
#### 4.2.1 Baseline Results  
The baseline results of the above three sets are listed in Table 2. It is worth noting that all the character error rate (CER) and WER results are derived with the official scripts in ESPnet. Anyone can easily repeat the baseline results by following the ESPnet [17] scripts. However, one of the issues of ESPnet results is that their performances do not reach those of the state-of-the-art hybrid HMM/DNN systems. According to the explanation of ESPnet, applying this technique to these English datasets will encounter the issue of long sequence lengths, which requires a large computational cost and makes it difficult to train a decoder network. Furthermore, end-to-end speech recognition usually requires more data than the traditional model to achieve the best performance.  
For a fair comparison, we will try our best to list the comparable systems referred to in the literature. Thus, for WSJ, consistent with the discussion in [17], the comparable end-to-end method is described in [2]. This method utilizes the CTC as the training criterion and decodes based on the weighted finite-state transducers (WFSTs). It achieves 7.3% WER in the eval set, while the ESPnet reaches 5.4% WER in this data set.  
For AMI-IHM, we haven't found clear results of end-to-end method in the references. Therefore, we employ the result in Kaldi [26]. The best method uses the TDNN-LSTM acoustic model, lattice-free version of the maximum mutual information criterion [27] and the per-frame dropout techniques. Finally, it reaches 19.8% WER in dev set and 19.2% WER in eval set.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **WSJ** & **AMI** & **SWBD** \\ \hline Encoder type & Vggblstmp & blstmp & blstmp \\ Encoder layers & 6 & 8 & 6 \\ Subsampling & 4 & 4 & 4 \\ Attention & Location-aware & Location-aware & Location-aware \\ CTCWeight train & 0.5 & 0.5 & 0.5 \\ CTCWeight decode & 0.3 & 0.3 & 0.3 \\ RNNLM-unit & Word & Word & Word \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental configuration.  
For SWBD, the state-of-the-art ASR system is proposed in [28], which utilizes a blstmp acoustic model, spatial smoothing, and speaker adaptive modeling techniques. It achieves 12.0% WER in the eval set, training with 300h SWBD data. In the meantime, paying attention to the end-to-end methods, the best system [29] acquires 15.5% WER in the eval set with the Attention ED framework. When applying spectrogram augmentation methods, it reaches 10.6% WER, surpassing the performance of hybrid HMM/DNN systems.  
For CHIME-4, a traditional HMM/DNN method [30] reaches 5.42% WER in et05 real and 3.9% WER in et05 simu. However, in consideration of the end-to-end method, the authors in [31] acquire 22.7% CER in et05 real and 22.5% CER in et05 simu. Although the ESPnet results fall behind the HMM/DNN system, it reaches comparable results to the other end-to-end methods.  
In this paper, although the performances do not reach those of the start-of-the-art, we believe it has no relation with our key improvements. We pay more attention to the relative decrease of WER compared with the original hybrid CTC/Attention system.