With the development of deep learning and artificial intelligence, the application of lip recognition is in high demand in computer vision and human-machine interaction. Especially, utilizing automatic lip recognition technology to improve performance during social interactions for those hard of hearing, and pronunciation is one of the most promising applications of artificial intelligence in medical healthcare and rehabilitation. Lip recognition means to recognize the content expressed by the speaker by analyzing dynamic motions. Presently, lip recognition research mainly focuses on the algorithms and computational performance, but there are relatively few research articles on its practical application. In order to amend that, this paper focuses on the research of a deep learning-based lip recognition application system, i.e., the design and development of a speech correction system for the hearing impaired, which aims to lay the foundation for the comprehensive implementation of automatic lip recognition technology in the future. First, we used a MobileNet lightweight network to extract spatial features from the original lip image; the extracted features are robust and fault-tolerant. Then, the gated recurrent unit (GRU) network was used to further extract the 2D image features and temporal features of the lip. To further improve the recognition rate, based on the GRU network, we incorporated an attention mechanism; the performance of this model is illustrated through a large number of experiments. Meanwhile, we constructed a lip similarity matching system to assist hearing-impaired people in learning and correcting their mouth shape with correct pronunciation. The experiments finally show that this system is highly feasible and effective.
Keywords: GRU; MobileNet; attention mechanism; deep learning; lip recognition.