# Chinese Lip-Reading Research Based on ShuffleNet and CBAM
## 3 Experiment
### Recognition Results  
(1) Comparison with the current State-of-the-Art:  
In this section, we compare against two frontend types: ResNet-18 and MobileNet v2 on our dataset. ResNet is an extremely deep network using a residual learning framework that obtains a 28% relative improvement over the COCO dataset. MobileNet is a small, low-latency, low-power model designed to maximize accuracy and meet resource constraints for on-device applications. For a fair evaluation, all the models are combined with the same backend: Temporal Convolutional Network. The performances of different models are shown in Table 3.  
We can see that ResNet-18 has the best accuracy but consumes the most computing power. The accuracy and computational complexity of ShuffleNet v2 (1\(\times\)) [19] and MobileNet v2 are similar using our dataset. When the channel width of Shufflenet v2 is reduced, there is a decrease in accuracy. But after the attention module CBAM is inserted, the performance of ShuffleNet v2 (0.5\(\times\)) is almost the same as ShuffleNet v2 (1\(\times\)) with the decrease of FLOPs. In summary, ShuffleNet v2 (0.5\(\times\)) (CBAM; the one we propose) surpasses MobileNet v2 by 0.7% in recognition accuracy and reduces computation resources by almost 60% compared with ResNet-18, which has the highest accuracy. As is shown in Figure 12, the x-axis represents how many epochs the model has been trained for and the y-axis represents the accuracy of the model. The accuracy of the ResNet-18 network rises the most slowly in the beginning, because it has the most parameters and the best accuracy. The model ShuffleNet v2 (0.5\(\times\)) (CBAM) converges well when the number of epochs reaches 30 and achieves the second-highest accuracy compared with other models. As is shown in Figure 13, the x-axis represents how many epochs the model has been trained for and the y-axis represents loss values, which indicates the difference from the desired targets. The loss function of the ResNet-18 network decreases the fastest among  
Figure 12: Comparison of the accuracy on different models. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Top-1 Acc. (\%)** & **FLOPs \(\times\) 10\({}^{9}\)** & **Params \(\times\) 10\({}^{6}\)** \\ \hline ResNet-18 & 74.4 & 3.46 & 12.55 \\ MobileNet v2 & 69.5 & 1.48 & 3.5 \\ ShuffleNet v2 (1\(\times\)) & 71.3 & 1.73 & 3.9 \\ ShuffleNet v2 (0.5\(\times\)) & 68.4 & 0.89 & 3.0 \\ ShuffleNet v2 (0.5\(\times\)) (CBAM) & 71.2 & 1.01 & 3.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance of different models. The number of channels is scaled for different capacities, marked as 0.5\(\times\), 1\(\times\), and 2\(\times\). Channel widths are the standard ones for ShuffleNet V2, while the base channel width for TCN is 256 channels. the four architectures because it has the largest number of parameters. When the number of iterations reaches 30, the loss value of the ShuffleNet (0.5\(\times\)) (CBAM) network starts to change more slowly and becomes stable. It implies that the model fits the data well and at this point the parameters are optimal. Compared with ShuffleNet v2 (0.5\(\times\)), the ShuffleNet (0.5\(\times\)) (CBAM) has a lower lost value. The ShuffleNet (0.5\(\times\)) (CBAM) shares a similar loss with MobileNet but has a faster GPU speed which is already discussed in Table 2.  
A part of the results of different models on some words are shown in Table 4.