Generally speaking, college English teaching and learning encounters the problem of having to deal with too many tasks within too limited time given, which makes it difficult for teachers and students to meet the current College English Curriculum Requirements. In response to this problem, we have conducted a research entitled "College English Teaching and Learning Based on Students' Sufficient Preview and Autonomous Learning". This article is a case study of one (the third) of the four lessons. The article describes the originality of the project and the preparation and implementation of the classroom activities, and analyzes the characteristics of that classroom teaching and learning, and the roles, emotions and attitudes of both the teacher and the students involved.

This paper presents an in-depth study and analysis of adaptive proofreading of spoken English pronunciation in a wireless sensor network environment. This paper addresses the above problem by combining two common methods for controlling the transmission rate of sensing nodes maximizing network utility algorithm and congestion control mechanism. Firstly, the transmission rate of one-hop nodes at a distance from the aggregation node is dynamically adjusted by the increasing exploration algorithm under the premise of unknown link transmission capacity, while the transmission rate of one-hop nodes is proportionally allocated to multihop nodes in multihop nodes by the congestion control mechanism based on the average reception success rate of the link. A design framework for a speech recognition system with complementary offline recognition and online recognition based on the C/S model is proposed, and a speech recognition system in swarm intelligence awareness is implemented based on the Sphinx engine. The client side implements the speech recognition of decoder in the offline state, and the server side provides the functions of recognition consistency detection, model adjustment training, monitoring, and recommendation in the online state as well as the interface for external access. The scene adaptation module effectively improves the speech recognition system’s speech recognition correct rate under different scenes, and the discourse topic recognition module verifies the recognition effectiveness of the speech recognition system under different discourse topics, which can meet the requirements of users’ personalized speech input.