scholarly journals Research on Business English Translation Architecture Based on Artificial Intelligence Speech Recognition and Edge Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yunwei Xu
Keyword(s):  
Artificial Intelligence ◽  
Speech Recognition ◽  
News Media ◽  
English Translation ◽  
Chinese Culture ◽  
Online News ◽  
Edge Computing ◽  
Target Language ◽  
Translation System ◽  
Business English

In today’s society, the continuous deepening of international cultural integration has become the background of the times. China has become more and more closely connected with the world, and many physical or online news media have become a platform for China to receive world information and spread Chinese culture. Business English translation is therefore valued by translation researchers and translators. Aiming at the shortcomings of current business English translation research, this paper designs and develops a business English translation architecture based on artificial intelligence speech recognition and edge computing. First of all, considering the relevance and complementarity between speech and text modalities, this paper uses the deep neural network feature fusion method to effectively fuse the extracted monomodal features and perform speech recognition. Secondly, adopt the edge computing method to establish the business English translation system architecture. Finally, the simulation test analysis verifies the efficiency of the business English translation framework established in this paper. Compared with the existing methods, our proposal improved the accuracy than others at least 10% and the time of model building also decreased obviously. The purpose of this research is to discuss how to deal with the many differences between the source language and the target language, and how to enhance the readability of the translation and meet the reader’s cultural cognition and needs.

scholarly journals Networked Artificial Intelligence English Translation System Based on an Intelligent Knowledge Base and Translation Method Thereof

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shuping Ren
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Base ◽  
Structure Analysis ◽  
English Translation ◽  
Language Translation ◽  
Translation Method ◽  
Translation System ◽  
Chinese Translation ◽  
Lexical Structure ◽  
Traditional Language

Language translation is often conducted in work and study. Traditional language translation is based on lexical structure analysis. However, natural language is not so standardized, which causes this translation method to have fundamental defects, no matter how much the algorithm is improved. The translation results and human translation will be very different. This paper mainly studies the networked artificial intelligence. The English translation system and translation methods are based on a smart knowledge base. Bringing an example of English-Chinese translation to suggest translations according to the intelligent knowledge base explains in detail the principle of intelligent knowledge-based translation and the advantage of this translation method compared with the traditional translation method based on lexical structure analysis. In the experiment of this paper, when the variance is 2/N, 30/N, 100/N, and 2N, it is the experimental data for an in-depth study. When the variance is 2/N, 30/N, and 100/N, the result is the same as that when the variance is 0.5; the result when the variance is 2N also conforms to the trend in the tables, which is close to the effect of the smoothing algorithm, which verifies the effectiveness of the system in this paper.

scholarly journals Language To Language Translation System

2020 ◽  
pp. 289-293
Author(s):  
Ms Pratheeksha ◽  
Pratheeksha Rai ◽  
Ms Vijetha
Keyword(s):  
Speech Recognition ◽  
Machine Translation ◽  
Automatic Speech Recognition ◽  
Speech Synthesis ◽  
Language Translation ◽  
Target Language ◽  
Translation System ◽  
Text To Speech ◽  
Source Language ◽  
Text To Speech Synthesis

The system used in Language to Language Translation is the phrases spoken in one language are immediately spoken in other language by the device. Language to Language Translation is a three steps software process which includes Automatic Speech Recognition, Machine Translation and Voice Synthesis. Language to Language system includes the major speech translation projects using different approaches for Speech Recognition, Translation and Text to Speech synthesis highlighting the major pros and cons for the approach being used. Language translation is a process that takes the conversational phrase in one language as an input and translated speech phrases in another language as the output. The three components of language-to-language translation are connected in a sequential order. Automatic Speech Recognition (ASR) is responsible for converting the spoken phrases of source language to the text in the same language followed by machine translation which translates the source language to next target language text and finally the speech synthesizer is responsible for text to speech conversion of target language.

scholarly journals Research on Business English Translation Framework Based on Speech Recognition and Wireless Communication

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Leida Wu ◽  
Lianguan Wu
Keyword(s):  
Speech Recognition ◽  
Wireless Communication ◽  
Error Correction ◽  
English Translation ◽  
Language Translation ◽  
Functional Requirements ◽  
Business English ◽  
Correction Rate ◽  
Translation Error ◽  
Processing Module

In order to improve the accuracy of English translation, reduce the error rate of translation results, and increase the correction rate of translation, this paper proposes a business English translation architecture design based on speech recognition and wireless communication. The architecture is partitioned according to the functions of the overall system design, and the voice acquisition module, voice processing module, and peripheral circuit module are designed according to functional requirements. Among them, speech recognition helps users to perform language translation to reduce the possibility of errors in the translation process. At the same time, it uses wireless communication technology to construct a business English translation corpus to meet the personal needs of users. The paper also uses an improved translation model for translation error correction and intelligent proofreading, which improves the reliability of translation results. Experimental results show that the system has a high error correction rate and error correction rate, and the translation results have a certain degree of reliability, which fully verifies the effectiveness and application value of the system.

Application research of artificial intelligence English audio translation system based on fuzzy algorithm

2021 ◽  
pp. 1-10
Author(s):  
Erying Guo
Keyword(s):  
Artificial Intelligence ◽  
Speech Recognition ◽  
Speech Signal ◽  
Training Data ◽  
Utilization Rate ◽  
Translation System ◽  
Fuzzy Algorithm ◽  
Recognition Model ◽  
Language System ◽  
Cross Language

With the development of globalization, people’s demand for English audio interaction is increasing. In order to overcome the shortcomings of traditional translation methods in grammatical variables, such as semantic ambiguity, quantifier errors, low translation accuracy, improve the quality and speed of English translation, and get more accurate and speed guaranteed translation, this study proposes an artificial intelligence English audio translation cross language system based on fuzzy algorithm. In this experiment, the collected analog speech signal is converted into a digital speech signal, and then, the speech features are modeled and digitized, and the whole set of speech samples are integrated and modified to eliminate the interference caused by noise as far as possible. After that, the collected voice will be stored in the text format, and then the text will be translated to achieve English audio translation. The DNN-HMM speech recognition model and the traditional GMM-HMM speech recognition model are used to preprocess the original corpus, and the accuracy of the corpus processing is compared. After that, the accuracy and utilization of the fuzzy algorithm are evaluated between the first type TSK and the second type TSK. For speech synthesis in which the corpus lacks language, it is meaningful to explore the least amount of training data for the synthesis of acceptable speech. The experimental results show that the accuracy of the fuzzy algorithm is about 97.34%, and the utilization rate is about 98.14%. The accuracy rate of type 1 and type 2 algorithms are about 85.77% and 76.87% respectively, and the utilization rate is about 83.25% and 78.63% respectively. The fuzzy algorithm based artificial intelligence English audio translation cross language system is obviously better than the other two algorithms.

Optimal system design of language training strategy based on artificial intelligence

2020 ◽  
pp. 1-11
Author(s):  
Jianling Guo ◽  
Jia Liu
Keyword(s):  
Artificial Intelligence ◽  
Speech Recognition ◽  
Foreign Students ◽  
Chinese Culture ◽  
New Technologies ◽  
New Technology ◽  
Rapid Development ◽  
Chinese Students ◽  
Training Strategy ◽  
Artificial Intelligence Technology

With the rapid development of China’s economy and the rapid increase in the number of Chinese learners in recent years, Chinese fever has become a common phenomenon in the global language exchange system. However, in the absence of foreign Chinese teachers at present, the development of Chinese new technology, this document uses the new computer technology to establish a Chinese teaching platform. The system is based on speech recognition technology to help foreigners learn spoken language, manuscript recognition technology and Chinese letters. foreign students in artificial intelligence technology, simulation of Chinese foreign education and training. The establishment of this system will not only help foreign students to solve the problem of Chinese learning successfully, but also make important contributions to the learning of Chinese students. Some new technologies, such as speech recognition, will be recognized by more and more Chinese students with the development of new technologies and the renewal of the system, and will make the greatest contribution to the promotion of Chinese culture.

scholarly journals A Diachronic Study on Translation Strategies of Culture-specific Items --- With the Translation of Measurement Unit in Howard GoldBlatt’s Works as An Example

2018 ◽  
Vol 2 (6) ◽  
Author(s):  
Fengtian Sun
Keyword(s):  
English Translation ◽  
Chinese Culture ◽  
Target Language ◽  
Measurement Unit ◽  
Translation Strategies ◽  
Diachronic Study

Abstract: The translation of Culture-specific Items (CSI) has been a challenge for translators as well as cited examples for untranslatability. With the analysis of the strategies employed on the Chinese-English translation of measurement unit by Howard Goldblatt in his translation works of different times, this paper discusses the transition from “untranslatable” to “translatable” of certain CSIs. Translation strategies for CSI are also proposed with the consideration of how Chinese culture being introduced into the outside world. The study shows that although Goldblatt clearly advocates “reader-centered” translation, that is, the translator should translate for the target language reader, and emphasizes that the translator can only translate and be responsible for the target language reader, we can still see his efforts to introduce Chinese culture to American readers through his translation.

THE SOURCE OF ERRORS IN INDONESIAN-ENGLISH TRANSLATION

Jurnal KATA ◽  
2017 ◽  
Vol 1 (2) ◽  
pp. 192 ◽  
Author(s):  
Silvia Utami
Keyword(s):  
Error Analysis ◽  
English Translation ◽  
Target Language ◽  
Incomplete Knowledge ◽  
Descriptive Research ◽  
Grammatical Errors ◽  
Translation Errors

<p>This research aimed to identify types of translation errors and to find out the sources of errors (interlingual and intralingual errors) in Indonesian-English translation written by the students. The type of this research was descriptive research which used Error Analysis procedures to identify and analyze the students’ error. The findings showed that the types of grammatical errors made by the students in their translation were three types, namely global errors, local errors, and other errors. The most frequent error made by the students was local errors and the fewest error made by the students was other errors.  Then, this research revealed that mostly errors occurred in students’ translation were caused by intralingual error. Meanwhile, only few errors were caused by interlingual error. The errors occured due students’ incomplete knowledge of the target language.</p>

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