# Multi-Scale Feature Learning for Language Identification of Overlapped Speech
## 4 Experiments and Discussion
### Experimental Results and Analysis  
In this section, we used the SE-Res2-Net-CBAM-BiLSTM language identification model proposed in this paper to carry out experiments on the Oriental Corpus and our own multilingual cocktail party corpus. There were three experimental tasks. Task 1: Three different models were selected and compared using the language spectrogram as input features, and their experimental results are shown in Figure 9.  
As can be seen in Figure 9, for the four different models, the SE-Res2 Net-CBAM-BiLSTM model had higher values than the other three models for the evaluation metrics corresponding to the AP17-OLR dataset, with an accuracy of 97.64%. The results in this paper show an accuracy improvement of about 3% compared to the baseline model CNN-CBAM-BiLSTM. This is because we replaced the CNN network with the SE-Res2 Net network module in the baseline model. The SE-Res2 Net network is a new multi-scale backbone network structure that can represent multi-scale features at a fine-grained level and increase the receptive field range of each network layer. These advantages can help SE-Res2 Net-CBAM-BiLSTM networks to better understand and extract features so as to achieve better performance in language identification tasks. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Confusion Matrix for Target and**} & \multicolumn{3}{c}{**Predicted Value**} & \\ \cline{3-5} \multicolumn{1}{c}{**Non-Target Languages**} & \begin{tabular}{c} **Target** \\ **Languages** \\ \end{tabular} & \
\begin{tabular}{c} **Non-Target** \\ **Languages** \\ \end{tabular} &
\begin{tabular}{c} **Total** \\ **Languages** \\ \end{tabular} \\ \hline \multirow{2}{*}{True value} & Target languages & \(TP\) & \(FP\) & \(P\) \\ \cline{2-5}  & Non-target languages & \(TN\) & \(FN\) & \(N\) \\ \hline \multicolumn{2}{c}{Total} & \(T\) & \(F\) & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of statistical identification. We conducted experiments on the CNN-CBAM-BiLSTM model and the SE-Res2 Net-CBAM-BiLSTM model using an Oriental language dataset. Figures 10 and 11 show the accuracy and loss changes in the training and validation sets. Figure 10 shows the CNN-CBAM-BiLSTM model's curves, and Figure 11 shows the SE-Res2 Net-CBAM-BiLSTM model's curves. The blue curves are for the training set, and the yellow curves are for the validation set. Figures 10 and 11 show that on the CNN-CBAM-BiLSTM model, the accuracy and loss curves fluctuate, and thus, affect the stability of the model. In contrast, on the SE-Res2 Net-CBAM-BiLSTM model, the accuracy and loss curve fluctuations were relatively minor and more stable. Figure 11: Accuracy loss curve of SE-Res2 Net-CBAM-BiLSTM model. Figure 10: Accuracy loss curve of CNN-CBAM-BiLSTM model. Figure 9: Comparison of experimental results under different models. Task 2: In this section, a comparative test on the multilingual cocktail dataset with a 100% overlap rate was carried out on the baseline model and the improved model. The accuracy of the baseline model CNN-CBAM-BiLSTM was 64%, and the specific results are shown in Table 4.  
The improved SE-Res2 Net-CBAM-BiLSTM model was used on a multilingual cocktail party scenario in which the target language weight was 1.2 and the non-target language weight was 1. When the overlap was set to 100%, the accuracy of the model was 75%, as shown in Table 5.  
As seen in Tables 4 and 5, after using the model proposed in this article, the accuracy of the model on the multilingual cocktail party dataset was significantly improved by 11% compared to the baseline model. Thus, this also validates the effectiveness of the improved model proposed in this paper. Task 3: Comparison of loss functions. In the previous language identification model, cross-entropy loss was used for classification, but it needed to take into account the problems of unbalanced data and confusion of languages. We used focal loss [36] to solve this problem effectively and improved the model performance.