# Multi-Scale Feature Learning for Language Identification of Overlapped Speech
## 3 Algorithm Model Structure
### Algorithm Module
#### 3.1.5 SE-Res2 Net Module  
In 2020, [PERSON] et al. [6] proposed the squeeze-and-excitation network (SENet) to obtain different weights on the channel dimension of the feature map to highlight important features and ignore useless features. Figure 5 shows the structure of the SE module network.  
The diagram above shows the network structure of the SE module. Given an input X with a feature channel count of C, a feature with a feature channel count of C is obtained by a series of general transformations. The previously obtained features are also rescaled by the following three operations: (1) The squeeze operation reduces the features to a single number per channel by compressing them spatially. This gives a global view of each channel. The output has the same dimension as the input channels. (2) The excitation operation is a gating mechanism similar to that in recurrent neural networks. The weights of each feature channel are generated by a parameter \(W\), where the parameter \(w\) is learned to explicitly model the correlation between the feature channels. (3) The scaling operation is where the output of the weight by excitation is considered as the importance of each feature channel after feature selection. The weights of each feature channel are then multiplied one by one with the previous features to complete the original features and reconstructed in the channel dimension.  
To prevent channel grouping from losing inter-channel correlation, the output y of the Res2 Net module is fed into the SE module. The network structure is shown in Figure 6. In this module, the features \(y\in R^{w\times h\times b\times c}\) are first compressed into \(y^{\prime}\in R^{1\times 1\times 1\times c}\) using global average pooling. Then, the correlation between channels is fitted using fully connected layers and finally normalized using a sigmoid activation function. Thus, the weight vector of the channels is \(f^{c}=\sigma(FC(\delta(FC(y^{\prime}))))\), where \(FC\) denotes the fully connected layer, \(\sigma\) denotes the ReLU function, and \(\delta\) denotes the sigmoid function. The output of the SE module is \(f=f^{\prime}+x\). The rescaling of the original features in the channel dimension is implemented inside the residual unit to complete the feature adjustment.  
Figure 4: ResNet and Res2 net network infrastructure diagram. (**a**) ResNet infrastructure diagram; (**b**) Res2 Net infrastructure diagram.  
Figure 5: SE module structure.  
finally, the input \(x\) of the residual unit is connected to the output \(f^{\prime}\) of the residual unit by means of a jump connection to obtain the output of the SE-Res2 Net module as \(f^{\prime}=f^{c}\cdot y\). The fusion of the SE module after the \(1\times 1\) convolution allows the advantages of the SE module to be enhanced by reassigning different weights to the channel features, eliminating invalid features, and allowing the single-layer features to be used to maximum effect. The SE-Res2 Net module designed in this paper can emphasize the residual mapping, promote the convergence of the network, and improve the stability of the model.