Abstract
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.
Highlights
Wireless sensor network technology quantifies the information of the physical world into digital information through the rich variety of sensors carried by itself and connects it with the digital world through wireless communication devices, which is one of the important basic technologies of industrial informatization
Information collection is the basic task of wireless sensor networks, in terms of the current development trend, the current sensor devices continue to enrich, able to obtain more useful information from the physical world, but to meet the large-scale use of scenarios sensor nodes mostly use small low-cost devices, which leads to the sensor nodes in the function of the general computing power, storage capacity, and communication capacity is limited [1]
This paper focuses on high-throughput transmission mechanisms under high-load application scenarios in wireless sensor networks
Summary
Wireless sensor network technology quantifies the information of the physical world into digital information through the rich variety of sensors carried by itself and connects it with the digital world through wireless communication devices, which is one of the important basic technologies of industrial informatization. Many the masses of mobile phones or tablets and other intelligent terminal devices consciously or unconsciously collect various aspects of sensor data as well as voice data as the input data set for group intelligence perception applications, which can do analysis and prediction of complex scenes or behaviors, with far-reaching scientific research significance and application value. It can only meet the initial needs of industrial informatization, but still cannot meet the collaborative interaction of heterogeneous systems when collecting information in the physical world, and realize the deep integration of people, things, and service networks. To simplify the subsequent processing work as well as to facilitate the use by others, the proposed end-toend language recognition algorithm with fused language models is implemented in a process, and a website based on the Django framework is built; the website can complete the offline and online recognition of speech documents and test the practicality of the improved algorithm in this paper
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