Abstract
This paper is aimed at the problems of low accuracy, long recognition time, and low recognition efficiency in English speech recognition. In order to improve the accuracy and efficiency of English speech recognition, an improved ant colony algorithm is used to deal with the dynamic time planning problem. The core is to adopt an adaptive volatilization coefficient and dynamic pheromone update strategy for the basic ant colony algorithm. Using new state transition rules and optimal ant parameter selection and other improved methods, the best path can be found in a shorter time and the execution efficiency can be improved. Simulation experiments tested the recognition rates of traditional ant colony algorithm and improved ant colony algorithm. The results show that the global search ability and accuracy of improved ant colony algorithm are better than traditional algorithms, which can effectively improve the efficiency of English speech recognition system.
Highlights
Voice is a commonly used and important information when communicating between people
After analysing the basic principle of basic ant colony algorithm applied to time planning, the technical difficulty lies in how to use a series of Complexity methods to increase the convergence speed of ant colony, find the optimal matching path between the test template and the reference template in the shortest time, so as to improve the recognition rate of the system, which directly determines the quality of the algorithm in the actual application process [11]. erefore, this paper proposes an improved ant colony algorithm, such as a new ant colony state transition method, dynamic information request update rules, and better ant colony parameter selection, which effectively solves the template matching problem in dynamic time planning and achieves good results
E experiment is completed on the platform of Windows 7 operating system, matlabr2016a. e experimental data composition is as follows: recording is in a quiet laboratory environment, a total of 30 people, respectively, add different signal-to-noise ratios (SNR) (15 dB, 20 dB, 25 dB, and 30 dB), and the noise used in the experiment is additive Gaussian noise
Summary
Voice is a commonly used and important information when communicating between people. Voice recognition technology is a technology that allows machines to convert human voice signals into corresponding commands through recognition and understanding. Voice is a common and important information used in communication between people [1]. When people need to express some kind of information, they must use the voice signal that carries the information. E speech signal contains information about semantic content and contains information about the speaker’s personal identity. Speech recognition is gradually becoming the key technology of human-computer exchange in information technology. With the improvement of continuous speech recognition rate, speech recognition input has gradually become one of the important forms of computer input
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
