Research On Application Of Improved Artificial Bee Colony Algorithm In Speech Recognition

Artificial Bee Colony Algorithm(ABC)is an emerging group intelligent optimization algorithm inspired by bee colony behavior.Because of its strong global search ability,the global optimal solution can be obtained with a relatively high probability in the optimization process,and it has received extensive attention from scholars at home and abroad.Automatic Speech Recognition(ASR)is the core technology for realizing human-computer interaction.The ASR model is an important part of speech recognition.Therefore,how to make the model optimal is a research hotspot in the ASR field.ASR technology has achieved some good research results,but there are still some difficult technical problems in how to achieve more natural and smooth human-computer interaction.It is necessary for scholars to conduct in-depth research and exploration.This paper presents an improved ABC algorithm and applies it to the ASR model.The experimental results show that the ABC algorithm and the improved ABC algorithm have some excellent characteristics.The main research results in this paper are as follows:1)In the ABC algorithm,the search behavior of bees in three stages is not exactly the same,but the same search equation is used,and the solution search formula designed in each stage is more suitable for global search,while the local search ability is weak.Therefore,the convergence speed and convergence accuracy of the algorithm need to be improved when searching in a large search space.Therefore,to improve the performance of the algorithm,different search equations must be adopted for different search phases.Aiming at the deficiencies of the above ABC algorithm,an adaptivedual search ABC algorithm(ADSABC)is proposed.A new search equation was proposed at the stages of bee-snapping and bee-watching,which made the bee searching continuously change its search space during the search process,speeding up the search efficiency,effectively improving the performance of the algorithm,and at the same time,in order to enable the algorithm to jump out better.Local optimisation,adding a mutation factor to the bee's search phase.The optimization results of standard test functions show that the convergence accuracy and convergence speed of the improved algorithm are improved.2)Wavelet Neural Network(WNN)has a high degree of nonlinear mapping and strong adaptive capabilities.It has been widely used in speech recognition.However,WNN suffers from local minima,slow convergence,and even the disadvantages of non-convergence and weak global search capabilities,aiming at the above-mentioned shortcomings of WNN,proposes using ADSABC algorithm to optimize the WNN network and apply it to the speech recognition system,and improve the performance of the WNN speech recognition system and other group intelligent optimization algorithms.Compared with the recognition results of WNN speech recognition system,the average recognition rate has increased by 5.07%,highlighting the feasibility and superiority of the proposed method.