# Multi-Scale Feature Learning for Language Identification of Overlapped Speech
## 3 Algorithm Model Structure  
### Algorithm Module  
#### 3.1.1 SE-Res2 Net-CBAM-BiLSTM Network  
This study proposes a SE-Res2 Net-CBAM-BiLSTM language identification method that combines the compressed excitation multi-scale residual (SE-Res2 Net) module and CBAM. The SE-Res2 Net module can extract features in parallel through multiple branches  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Proposed Algorithm** & **Features** \\ \hline GMM-UBM [12] & The method of combining SDC features with a Gaussian mixture model–general background model is proposed, but it requires a large number of parameters, and the training data are usually insufficient. \\ \hline GMM-SVM [14] & SVM algorithm is proposed to classify the GMM mean super-vector of speech. \\ \hline i-Vector [15] & Language identification using i-vector obtained from speech combined with a back-end discrimination algorithm \\ \hline DBF [16] & Replaces the traditional GMM-UBM combined with acoustic features, which can effectively characterize linguistic information and make language more easily distinguishable. \\ \hline DNN [5] & First proposed application of deep neural networks to language identification tasks. \\ \hline LSTM-RNN [17] & This method effectively solved the problem of gradient disappearance in RNNs. \\ \hline BiLSTM [18] & This method effectively takes into account the past and future information of speech. \\ \hline GRU [19] & GRU has a more straightforward structure and improved recognition rate compared to the Bi-LSTM network. \\ \hline CNN [20] & CNN extracts local features of speech signals and effectively improves language identification. \\ \hline Attention mechanism [21] & Through the attention mechanism, the more valuable information for language identification in speech features is obtained, and the effect of language identification is improved. \\ \hline CRNN [22] & The combined network extracts richer speech feature information, thus improving the accuracy of language identification. \\ \hline Transformer [24] & Transformer architecture can perform parallel computation and extract deeper and richer feature information. \\ \hline Pre-trained [25] & The pre-trained model can obtain better discriminative representation and make full use of unsupervised data. \\ \hline BERT-based [26] & The study extended the original BERT model by taking the phonetic posterior grams (PPGs) derived from the front-end phone recognizer as input. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of different language identification technologies.  
and adaptively weigh different channels so as to improve the performance and generalization ability of the network. These advantages can help the SE-Res2 Net-CBAM-BiLSTM network to better understand and extract image features so as to achieve better performance in language identification tasks. The network block diagram is shown in Figure 1.  
Specifically, on the basis of the CBAM-BiLSTM network, the SE-Res2 Net multi-scale feature extraction module was added, and a language identification network with a multi-scale mechanism was built to improve the network's feature expression ability. First, feature extraction is performed to convert speech into corresponding spectral features, and then the spectral features are used as input to extract multi-scale features through the SE module and Res2 Net module. In the multi-scale extraction module, the network's description ability is further enhanced, and the feature representation is clearer. Then, it is sent to the CBAM module to assign different weights to the channel and spatial dimensions of the feature map so as to derive features useful for language identification. Finally, the output of the CBAM module is sent to the tile layer through the full connection layer, and finally, the language classification is realized through the softmax classifier.