Intelligent Learning Model of Financial Spoken English Teaching based on BPTT algorithm and LSTM Network Model

Abstract

It is of great research value and practical significance to use new technology to improve the accuracy of English speech recognition and apply the system to mobile platforms for users to use. The main content of this paper is the long-term and short-term memory, and the current decoding part is applied to the Android platform, and the performance of the program is analyzed. Neural networks converge slowly, making learning long-term memory difficult. In the experiment, the BPTT algorithm is used to analyze the problem of error elimination in traditional recursive networks. Combining BPTT algorithm in LSTM network to solve the problem of traditional error elimination and improve speech recognition rate. In addition, this paper uses a new LSTM recurrent neural network to study the implementation of LSTM network on Android platform. Finally, this paper designs a comparative experiment to analyze the efficiency of oral English recognition. The results show that the research algorithm of this paper has certain effects.