# A Deep Neural Network Model for Speaker Identification
## 3 Results
### Model Results  
For the experiments, the total of 141,925 utterances from the Aishell-1 dataset was divided into a 90% training set, 5% validation set, and 5% test set. To visualize the learning efficiency of the models, one training progress of the proposed deep GRU network model with the Aishell-1 dataset is shown in Figure 8, where the left block presents loss vs. training epochs and the right part represents the accuracy vs. training epochs. To evaluate our proposed deep GRU model, we also conducted experiments with a 2-D CNN network  
Figure 9: Loss and accuracy of the 2-D convolutional neural network (CNN) network model with the Aishell-1 dataset. The left part of the figure describes the loss vs. training epochs, and the right part describes the accuracy vs. training epochs. model, traditional RNN, and LSTM network models, and mainly discussed the experiment results using the Aishell-1 dataset and Gaussian white noise-added Aishell-1 dataset. Table 2 reports the overall accuracy of different models we tried in our different research periods. The reported overall accuracy ranged from 95.42% to 98.96% with the Aishell-1 dataset, the proposed deep GRU model achieved the highest recognition accuracy in all network models. Figure 10 shows the above results in a column diagram format, it can be seen from the comparison that the proposed deep GRU network model has high robustness. We also conducted some experiments with several existing successful network models for comparison, and the experiment results are shown in Table 3. It is clear that when Gaussian white noise was added into the testing data, the recognition accuracy of other models dropped significantly; contrasting, the proposed deep GRU network model still achieved a robustly high recognition accuracy. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Types of Model** & **Datasets** &
\begin{tabular}{c} **Overall** \\ **Accuracy (\%)** \\ \end{tabular} & **Real Time per Epoch** \\ \hline \multirow{2}{*}{2-D CNN} & Aishell-1 & 95.42\% & \multirow{2}{*}{1035 s} \\  & noise-added Aishell-1 & 48.57\% & \\ \hline \multirow{2}{*}{CNN + original RNN} & Aishell-1 & 96.67\% & \multirow{2}{*}{1154 s} \\  & noise-added Aishell-1 & 55.29\% & \\ \hline \multirow{2}{*}{CNN + RNN-LSTM cell} & Aishell-1 & 97.09\% & \multirow{2}{*}{1065 s} \\  & noise-added Aishell-1 & 70.42\% & \\ \hline \multirow{2}{*}{Proposed Deep GRU} & Aishell-1 & 98.96\% & \multirow{2}{*}{980 s} \\  & noise-added Aishell-1 & 91.56\% & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance of classification accuracy of models. The noise-added Aishell-1 in Table 2 is the same as the Aishell-1 with Gaussian white noise added in Table 3, and it refers to the clean Aishell-1 dataset with Gaussian white noise added. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Datasets** & **Overall Accuracy (\%)** \\ \hline \multirow{2}{*}{Proposed Deep GRU} & Aishell-1 & 98.96\% \\  & Aishell-1 with Gaussian white & \\  & noise added & 91.56\% \\ \hline \multirow{2}{*}{MFCC-GMM [10]} & Aishell-1 & 86.29\% \\  & Aishell-1 with Gaussian white & \\  & noise added & 55.29\% \\ \hline \multirow{2}{*}{CNN [8]} & Aishell-1 & 92.45\% \\  & Aishell-1 with Gaussian white & \\  & noise added & 62.39\% \\ \hline \multirow{2}{*}{RNN [9]} & Aishell-1 & 89.67\% \\  & Aishell-1 with Gaussian white & \\  & noise added & 58.29\% \\ \hline \multirow{2}{*}{Multimodal LSTM [11]} & Aishell-1 & 96.25\% \\  & Aishell-1 with Gaussian white & \\  & noise added & 70.42\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison with several existing models. During our experiments, we tried to use RNN and its variants in the model. After the results, we made further analysis on the parameters of the model and training time. Tables 4-6 list the detailed structure information and number of training parameters for those models we tried in our different research periods. Table 2 shows the time consumptions of one epoch training for different models.