# The XMUSPEECH System for Accented English Automatic Speech Recognition
## 4 Conclusions  
In this paper, we explored various approaches to improve the accuracy of the accented ASR system. For the acoustic model, we tried TDNN-F, CNN-TDNNF-Attention, and  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**System**} & \multirow{2}{*}{**Features**} & \multirow{2}{*}{**Embeddings**} & **WER (\%)** & **WER (\%)** \\  & & & **on Dev** & **on Eval** \\ \hline Baseline (TDNN-F) & MFCC & - & 9.12 & 9.31 \\ Multistream CNN & MFCC + Pitch & accent-xvectors & 6.95 & 7.74 \\ Multistream CNN & & accent-xvectors & **5.41** & **5.99** \\ +LM rescoring & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Effect of language model rescoring. (The best result are highlighted in bold.)  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Embeddings**} & **Dev** & **Eval** \\ \hline
[M1] w/o embeddings & 8.86 & 9.08 \\
[M2] Spk-ivectors & 7.18 & 8.01 \\
[M3] Accent-ivectors & 7.17 & 7.95 \\
[M4] + spk-ivectors & 7.02 & 8.02 \\ \hline
[M5] Spk-xvectors & 7.04 & 7.76 \\
[M6] Accent-xvectors & 7.02 & 7.89 \\
[M7] + spk-xvectors & **6.95** & **7.74** \\ \hline \hline \end{tabular}
\end{table}
Table 3: WERs (%) achieved by multistream CNN with various input embeddings. (The best result are highlighted in bold.)  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Country**} & \multirow{2}{*}{**WER (\%)**} & **WER (\%)** \\  & **on Dev** & **on Eval** \\ \hline China & 8.10 & 9.29 \\ Japan & 4.08 & 4.01 \\ India & 6.79 & 7.24 \\ USA & 5.82 & 4.29 \\ Britain & 2.90 & 3.87 \\ Portugal & 4.42 & 4.57 \\ Russia & 6.23 & 6.83 \\ Korea & 4.86 & 3.99 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of different countries’ accents.  
multistream CNN; we found that multistream CNN had the best performance among them. Then we explored various speaker/accent embeddings to further improve the accuracy of accented ASR systems. Experiments showed that using embeddings, which capture the accent/speaker-relevant information as auxiliary inputs can significantly improve the accuracy of accented ASR system. Finally, a language model (LM) was trained for lattice rescoring. We chose the best model from previous attempts to perform LM rescoring and achieved a great improvement in comparison with our baseline.  
We will continue to improve the robustness of our acoustic model, including adopting layers with deeper levels and other CNN architectures; then we plan to experiment with End-to-End models as well. Furthermore, Chinese accented ASR is also a great challenge for the current ASR system; we will conduct experiments on Chinese and English accented speech recognition at the same time.  
Conceptualization, F.T., D.L., S.L., Q.H. and L.L.; methodology, F.T., D.L., S.X. and S.L.; software, D.L. and S.X.; validation, S.X. and T.L.; formal analysis, S.L.; investigation, S.X.; resources, Q.H. and L.L.; writing--original draft preparation, D.L., T.L. and F.T.; writing--review and editing, Q.H. and L.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 61876160 and No. 62001405) and in part by the Science and Technology Key Project of Fujian Province, China (Grant No. 2020 HZ020005).  
Not applicable.  
The authors declare no conflicts of interest.