## 5 Conclusions and Future Outlook  
This paper is a useful exploration of the Transformer model in speech recognition. In the transition from the DNN-HMM era of speech recognition to the End-To-End era, the multilayer hole causal convolution structure based on the TCDCN model of End-To-End technology provides a good attempt of the development of acoustic model and speech recognition. The biggest advantage of Transformer over the RNN lies in its speed advantage. Compared with the CNN, it can directly obtain global information. In the process of speech recognition, the increase in the receptive field is particularly important to the long-term dependence on the modeled speech signal. The model combines causal convolution and hole convolution, so that the receptive field increases exponentially with the training depth of the model, which also makes it stand out in many models. This paper uses the MFCC feature extraction method to extract the voice features, and then processes it through the deep convolutional neural network; to address the problem of the low correct classification rate of audio scenes, this paper adopts a hybrid model of the TCDCN, making full use of the feature that the CDCN can extract more information and Transformer's strong focus on time-related information, so that the classification of the hybrid model achieves an average accuracy of 89% on the UrbanSound8K dataset. The experimental results show that the TCDCN is better than the traditional neural network model, thus verifying that the combination of these two models can be well suited for audio scene classification tasks. As an application of the field of artificial intelligence, speech recognition has been widely used in real scenes, but the recognition scene still poses a challenge to the recognition technology. As a complete End-To-End model of the new paradigm of speech recognition, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Systems**} & \multirow{2}{*}{**Network Structure**} & \multirow{2}{*}{**Input Feature**} & \multicolumn{4}{c}{**VALAT**} \\ \cline{3-6}  & & & **Accuracy** & **F1** & **P** & **R** \\ \hline CNN & 3 conv + 2 Maxpooling & Mel-Spectrogram & 71\% & 0.71 & 63.4\% & 63.5\% \\ XGBoost & & log-mel & 83\% & 0.82 & 83.2\% & 82.4\% \\ CNN-XGBoost & 3 conv + 2 Maxpooling+XGBoost & log-mel & 89\% & 0.88 & 89.2\% & 87.2\% \\ CNN-LSTM & 4 conv + 2 Maxpooling + LSTM & Mel-Spectrogram & 84 & 0.81 & 83.1\% & 82.7\% \\ TCDCN & 8 conv + 4 Maxpooling & Mel-Spectrogram & 91 & 0.90 & 89.5\% & 88.7\% \\ CRNN(Dropout) & 3 conv + 2 Maxpooling + LSTM & Mel-Spectrogram & 73\% & 0.72 & 65.5\% & 65.7\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance of the proposed systems on the validation (Val.). Figure 8: Model training results-confusion matrix. it is a change in the field of speech recognition. Google, Baidu and other teams have also put forward their own End-To-End recognition models. Due to the limitations of time and the author's level, this article has not achieved enough comparisons of multiple corpus and multiple models in experimental comparison. In the future, we are also eager to make effective attempts on the End-To-End deep learning model based on the attention mechanism. Conceptualization, D.Z. and Z.L. ; methodology, D.Z. and Z.L. ; software, J.Z. ; validation, J.Z. and Z.L. ; formal analysis, J.Z. and Z.L. ; investigation, J.Z. and X.L. ; resources, D.Z. and Z.L. ; data curation, J.Z. ; writing--original draft preparation, J.Z. ; writing--review and editing, Z.L., J.Z. and X.L. ; visualization, J.Z. ; supervision, D.Z. and Z.L. ; project administration, D.Z. and Z.L. ; funding acquisition, D.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Office for Philosophy and Social Sciences Project \"Research on Cross-Modal Retrieval Model and Feature Extraction Based on Representation Learning\" (No. 17 BTQ62), Macao Science and Technology Development Fund under Macao Funding Scheme for Key R&D Projects (0025/2019/AKP).