# Weighted Cluster-Range Loss and Criticality-Enhancement Loss for Speaker Recognition
## 4 Experiments and Results
### Training Methodology  
The basic training configuration in this work is similar to that in [9]. The neural architecture of this work is presented in Table 5. To train the neural networks, we employ the SGD optimizer for each model with a momentum of 0.99. Each configuration will be trained for 100 epochs in total.  
To begin with, experiments for speaker identification are conducted. To explore the effect of the dual-path attention, we first test the architecture with the dual-path attention module and standard cluster-range loss. To explore the effect of our proposed loss functions, we replace the CRL with WCRL and CRL+CEL, respectively. Besides, we set the weighting factor of WCRL to different values so as to find the best weighting configuration for the model. All the mentioned loss functions are employed jointly with the softmax cross-entropy loss. The latter is only used for training a fully-connected (FC) classifier, while the former is utilized to train the speaker embeddings. To this end, the gradient from the fully-connected layer into speaker embedding is cut, and thus, the speaker embeddings are only updated by the proposed loss functions. The experiments are mainly conducted on the VoxCeleb1 dataset, and the CN-Celeb dataset is further employed to prove the effectiveness of our proposed approaches.  
As for speaker verification, considering that the WCRL is specially designed for the identification task, we only evaluate the dual-path attention module and CEL in this part. Experiments are first conducted on VoxCeleb1, and the configuration achieving the best performance will be further applied to VoxCeleb2. Note that the FC layer in the neural architecture is removed during this stage.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Layer Name** & **Kernel Size** & **Strides** & **Output Shape** \\ \hline Conv1 & \(3\times 3\), 64 & \(1\times 1\) & \(320\times 64\times 64\) \\ Max pool & \(3\times 3\), 64 & \(2\times 2\) & \(160\times 32\times 64\) \\ Res1 & \(\left[\begin{array}{c}3\times 3,64\\ 3\times 3,64\end{array}\right]\) & \(1\times 1\) & \(160\times 32\times 64\) \\ Conv2 & \(3\times 3\), 128 & \(1\times 1\) & \(160\times 32\times 128\) \\ Max pool & \(3\times 3\), 128 & \(2\times 2\) & \(80\times 16\times 128\) \\ Res2 & \(\left[\begin{array}{c}3\times 3,128\\ 3\times 3,128\end{array}\right]\) & \(1\times 1\) & \(80\times 16\times 128\) \\ Conv3 & \(3\times 3\), 256 & \(2\times 2\) & \(40\times 8\times 256\) \\ Res3 & \(\left[\begin{array}{c}3\times 3,256\\ 3\times 3,256\end{array}\right]\times 2\) & \(1\times 1\) & \(40\times 8\times 256\) \\ Conv4 & \(3\times 3\), 512 & \(2\times 2\) & \(20\times 4\times 512\) \\ DA1 & /, 512 & \(1\times 1\) & \(20\times 4\times 512\) \\ Conv5 & \(3\times 3\), 512 & \(2\times 2\) & \(10\times 2\times 512\) \\ DA2 & /, 512 & \(1\times 1\) & \(10\times 2\times 512\) \\ Average & / & / & 512 \\ Length Norm & / & / & 512 \\ \hline FC (for SI) & 512 \(\times\) 1251 & / & 1251 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The Residual (Res)-Dual-path Attention (DA) architecture. SI, Speaker Identification.