Optimization Simulation of English Speech RecognitionAccuracy Based on Improved Ant Colony Algorithm
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 pro...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/8858399 |
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| _version_ | 1832546788764024832 |
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| author | Lu Jing |
| author_facet | Lu Jing |
| author_sort | Lu Jing |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1f72edac5b0e43828eb28680d6479991 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-1f72edac5b0e43828eb28680d64799912025-02-03T06:47:00ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/88583998858399Optimization Simulation of English Speech RecognitionAccuracy Based on Improved Ant Colony AlgorithmLu Jing0School of Foreign Languages, Henan University of Animal Husbandry and Economy, Zhengzhou, Henan 450044, ChinaThis 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.http://dx.doi.org/10.1155/2020/8858399 |
| spellingShingle | Lu Jing Optimization Simulation of English Speech RecognitionAccuracy Based on Improved Ant Colony Algorithm Complexity |
| title | Optimization Simulation of English Speech RecognitionAccuracy Based on Improved Ant Colony Algorithm |
| title_full | Optimization Simulation of English Speech RecognitionAccuracy Based on Improved Ant Colony Algorithm |
| title_fullStr | Optimization Simulation of English Speech RecognitionAccuracy Based on Improved Ant Colony Algorithm |
| title_full_unstemmed | Optimization Simulation of English Speech RecognitionAccuracy Based on Improved Ant Colony Algorithm |
| title_short | Optimization Simulation of English Speech RecognitionAccuracy Based on Improved Ant Colony Algorithm |
| title_sort | optimization simulation of english speech recognitionaccuracy based on improved ant colony algorithm |
| url | http://dx.doi.org/10.1155/2020/8858399 |
| work_keys_str_mv | AT lujing optimizationsimulationofenglishspeechrecognitionaccuracybasedonimprovedantcolonyalgorithm |