## 1 Introduction  
With the arrival of the big data era, deep learning architecture has been widely regarded as one of the most important tools for the development of AI products. Deep learning has a very clear application direction, and compared with it, in the face of massive data, the traditional machine learning methods can be used in even more limited scenarios [1].  
However, if we want to make AI products based on deep learning architecture widely used in daily life, it is not only required that the system performances such as big data and cloud computing can handle the use scenarios, but also the hardware requirements of the product cannot be ignored. As the equipment needs to be commercialized, we should consider both performance and consumed resources. Therefore, aiming at this problem, this paper proposed a compression and acceleration of the network model and applied the method to the lip-language recognition system. Compared with existing models, our model showed plenty of obvious advantages.  
Voice interaction is commonly used in the part of human-computer interaction, which is effective and convenient. However, as human activities become more and more abundant, the accuracy of voice recognition declines drastically in some special scenes [2], such as the public place with mixed sounds and so on [3]. As a result of it, the lip-recognition system attracts our interest. Lip-reading methods are divided into two categories according to different feature extraction methods: one is the lip-recognition method based on traditional manual feature extraction and such methods include, for instance, hidden Markov models (HMMs) [4], the other is based on deep learning, which includes feed-forward networks, autoencoders, and convolutional neural networks (CNN) [5]. However, the construction of the lip-recognition system tends to depend on the performance of the convolutional neural network [6]. Networks with high recognition accuracy have many problems, such as a large parameter scale, high storage requirements, and complex computation at the same time. Therefore, in the case of limited equipment resources, how to build convolutional neural networks with the highest accuracy has become a research hotspot.  
An important factor affecting the network complexity is the size of the model. The Visual Geometry Group 16 (VGG16) has 140 million trainable parameters approximately, and if these parameters are stored in single-precision floating-point type, the model needs to occupy 530 MB of storage space. The model not only handles billions of floating points calculations, but also needs to store the results of intermediate procedures; the complexity of the model is obvious. The consumption of a large amount of computing resources accelerates the consumption of device hardware and reduces the system performance [7]. Therefore, it has become an important problem to compress convolutional neural networks and remove parameter redundancy while maintaining performance as much as possible. Model compression has been proposed to solve these problems, but traditional model compression still has many defects.  
This paper is mainly divided into the following parts. Firstly, we compared some classical network models and proposed that the VGG network used for pruning and the bidirectional long short-term memory (Bi-LSTM) [8] network used for processing the network. Secondly, we compressed and accelerated the VGG16 network. Then, we used the pruned network and the Bi-LSTM network to extract lip features [9]. Lastly, we introduced the datasets and verified the feasibility and correctness of our method from the three aspects of algorithm convergence, algorithm recognition, and speed algorithm accuracy. Moreover, the main contribution of this paper is that we applied the structured pruning to the lip recognition, which achieved good results.