Machine translation of English speech: Comparison of multiple algorithms

Yijun Wu,Yonghong Qin

doi:10.1515/jisys-2022-0005

Abstract

Abstract In order to improve the efficiency of the English translation, machine translation is gradually and widely used. This study briefly introduces the neural network algorithm for speech recognition. Long short-term memory (LSTM), instead of traditional recurrent neural network (RNN), was used as the encoding algorithm for the encoder, and RNN as the decoding algorithm for the decoder. Then, simulation experiments were carried out on the machine translation algorithm, and it was compared with two other machine translation algorithms. The results showed that the back-propagation (BP) neural network had a lower word error rate and spent less recognition time than artificial recognition in recognizing the speech; the LSTM–RNN algorithm had a lower word error rate than BP–RNN and RNN–RNN algorithms in recognizing the test samples. In the actual speech translation test, as the length of speech increased, the LSTM–RNN algorithm had the least changes in the translation score and word error rate, and it had the highest translation score and the lowest word error rate under the same speech length.

Highlights

IntroductionWith the development of the economy, the division of labor in society is becoming ever more detailed, and cooperation and exchange between different countries are increasing [1]
Globalization is a major trend in modern society
In To test the generalization performance of the three machine translation algorithms after training, i.e., the actual application performance, this study selected ten volunteers to read aloud English scripts with different word counts and translated the speech read by the volunteers using the three machine translation algorithms that have been trained respectively

Summary

Introduction

With the development of the economy, the division of labor in society is becoming ever more detailed, and cooperation and exchange between different countries are increasing [1]. Language communication is crucial, and the use of a conventional language that can be mutually understood can avoid misunderstandings and improve the efficiency of the division of labor. In the wave of globalization, it is sufficient for face-to-face daily communication, but on formal occasions and when a large amount of information needs to be exchanged, it is difficult for a single human interpreter to meet the increasing demand for language translation. Machine translation uses computers and Chinese–English thesauri to perform batch translation, but it is too rigid. Lee et al [5] used character-level convolutional networks to perform machine translation and found that the character-level convolutional network encoders

Results

Discussion

Conclusion