Modeling and Simulation of English Speech Optimization Teaching Recognition Based on Intelligent Edge Detection Algorithm

Abstract

English speech modeling is one of the key problems in the field of speech recognition. Its accuracy directly affects the performance of the English speech recognition system, and how to establish a more accurate acoustic model has always been the focus of researchers. This paper is based on the intelligent edge detection algorithm of the English speech optimization teaching recognition modeling simulation analysis to improve the accuracy of acoustic models such as parameters and the performance of continuous speech recognition system as the main purpose. In this paper, the accuracy of the neural network is improved on the premise of improving the training speed and decoding speed of the model. It proposes a new model and how to use the intelligent edge detection algorithm to build a complete English speech optimization teaching recognition system. The whole system includes the mobile terminal and the server, which realizes the most basic business logic of the speech recognition system. The experimental results of this paper show that from the point of view of the average recognition rate, the recognition effect of the optimized feature set has been further improved compared with the fusion feature. From the point of view of the recognition rate under different SNR environments, the recognition rate of the optimized feature set PCA-Features2 decreased by 0.47% compared with the FFPRLS_D + TEOCC feature set under 10dB10 words. Compared with the FFPLMS_D + TEOCC feature set, the recognition rate under 5dB10 words also drops by 0.47%.