Voice Detection and Deep Learning Algorithms Application in Remote English Translation Classroom Monitoring

Abstract

With the continuous development of cellular networks, the traffic from voice services increases gradually. The wireless sensor network (WSN) is a distributed network consisting of a large number of peripheral nodes distributed in the surveillance area. The nodes in the network complete it in a self-organizing form, and the sink node collects the data from each sensor node. When sending data, the nodes near the receiver will quickly run out of energy and cannot perform further transmission tasks. The resulting “power supply emptiness” problem has a great impact on network performance. Therefore, the power consumption of the network must be considered when designing the WSN routing algorithm. In order to effectively improve students’ academic performance and monitor students’ teaching conditions, the classroom remote monitoring system places two cameras in the university’s English translation classroom and uses technology to merge the information to execute the entire process. By recording the course, we can save the teacher’s classroom content and the student’s classroom performance and upload the recorded video in real time. In addition, the classroom remote monitoring system is a multiclient system, divided into teacher and student terminals. The user can log in, watch the video, and perform other necessary operations.