## 6 Conclusions  
In this paper, we propose a comprehensive study of the grid LSTM and the bidirectional recurrent structure. By taking advantage of these two strategies, we propose a novel architecture, called BiGridLSTM, which solves the gradient problems by constructing a unified framework for depth and time calculations and introducing gated linear correlations between neighboring cells in each layer of  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Model** & **Layer** & **Hidden Size** & **Params** & **CER (\%)** & **F1 (\%)** & **Sensitivity (\%)** & **Specificity (\%)** \\ \hline BiGridLSTM & 2 & 256 & 4,518,173 & 15.06 & 85.77 & 86.30 & 85.38 \\ BiGridLSTM & 5 & 128 & 2,925,213 & 13.15 & 88.02 & 88.53 & 87.21 \\ BiGridLSTM & 8 & 128 & 4,705,437 & **12.10** & **88.94** & **89.17** & **88.20** \\ \hline \hline \end{tabular}
\end{table}
Table 4: CER comparison of BiGridLSTM models of different numbers of layers. The bold data in table indicates the minimum CER and highest sensitivity, specificity, and F1 in the experiment.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Model** & **Layer** & **Hidden Size** & **Params** & **CER (\%)** & **F1 (\%)** & **Sensitivity (\%)** & **Specificity (\%)** \\ \hline LSTM & 2 & 256 & 835,869 & 21.64 & 79.50 & 80.11 & 78.93 \\ LSTM & 5 & 256 & 2,411,805 & 21.85 & 79.37 & 79.80 & 78.42 \\ GridLSTM & 2 & 256 & 2,262,813 & 20.44 & 81.03 & 81.46 & 79.78 \\ GridLSTM & 5 & 256 & 5,812,509 & 17.83 & 83.47 & 83.95 & 82.88 \\ BiGridLSTM & 2 & 256 & 4,518,173 & 15.06 & 85.77 & 86.30 & 85.38 \\ BiGridLSTM & 5 & 128 & 2,925,213 & **13.15** & **88.02** & **88.53** & **87.21** \\ \hline \hline \end{tabular}
\end{table}
Table 3: CER comparison of deeper models and baseline. The bold data in table indicates the minimum CER and highest sensitivity, specificity, and F1 in the experiment.  
each dimension. In addition, it adds the bidirectional structure in the recurrent layer to obtain context information at the current moment. We validated our BiGridLSTM model by performing a speech recognition task on the train-clean-100 data set of the English audio book LibriSpeech. The results show that our BiGridLSTM model outperforms all of the baseline models in the paper, and verify the assumption that using bidirectional structures to obtain contextual information is critical to achieving good performance. At the same time, the reduced CER can help completing structured and free-text queries on transcribed speech more accurately, which benefits the Semantic Web.  
For future work, on the one hand, we plan to add priority features to the bidirectional grid LSTM model; on the other hand, we combine time-frequency LSTM with bidirectional grid LSTM to improve the model recognition performance. We also want to study more in-depth models in larger tasks, such as large vocabulary speech recognition, machine translation, stock forecasting, and behavior recognition, etc.  
[PERSON] and [PERSON] proposed innovative idea; [PERSON] conceived the design of the solution, and wrote the first draft; [PERSON] worked on the implementation and improvement of the model; [PERSON] and [PERSON] verified the effect of the model; [PERSON] wrote the later versions of the paper; [PERSON] supervised the paperwork and provided writing advice; [PERSON] provided feasibility advice and hardware support.  
This research received no external funding.  
The authors declare no conflict of interest.