Design of English Intelligent Simulated Paper Marking System

Yang, Lina; Liu, Wei

doi:10.1155/2021/5529114

Cited by 3 publications

(3 citation statements)

References 26 publications

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“…e flow of the scoring system is shown in Figure 10. Preprepared acoustic modeling and music modeling are used as the answer model using speech recognition technology, and the differences in speech test and models are identified and scored, working with the scoring mechanism [20][21][22][23][24][25][26][27][28].…”

Section: Hmm-based Scoring Methodmentioning

confidence: 99%

An Auxiliary Teaching System for Spoken English Based on Speech Recognition Technology

Bao

2022

Scientific Programming

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Because the English language has always been inaccurate and seemed difficult to correct errors, this development has created a reputation based on improvements to the DWJ algorithm and HMM speech scores and correction mistake. In this paper, different signal characteristics are used using the DWJ algorithm: the Mel frequency cepstrum coefficient compares the standard speech library and the distance between the speech sample and the sample message received. The conversation deciphered the Viterbi code according to the HMM model, which was recognized and evaluated by posteriori probabilities. Finally, the professional data were used to fix the wrong phone to determine, score, and make repair mistakes. The results of the experiments show that the tests used in this article are reliable. The results of the experiment show that the standard English language proficiency in this article is reliable, which can provide students with timely, accurate, and objective assessment and teaching feedback, improving English language proficiency.

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Section: Hmm-based Scoring Methodmentioning

confidence: 99%

An Auxiliary Teaching System for Spoken English Based on Speech Recognition Technology

Bao

2022

Scientific Programming

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“…The process of intelligent correction is as follows: the teacher first assigns homework online, and the students upload it to the system after completing the homework. After that, the system uses artificial intelligence technology to automatically correct, generate learning reports and error sets, and then feedback to teachers, parents and students [1]. It can also recommend targeted topics according to the completion of students' homework.…”

Section: Introductionmentioning

confidence: 99%

Intelligent segmentation of subjective question area by removing template in intelligent marking

Xin

Zhang²

2023

Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022)

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With the rapid development of computer technology and communication technology, computers have been applied to all areas of people's lives. In the field of education, it is a necessary function for the intellectualization of the education system to realize the automatic marking of examination papers by computer. In intelligent marking, the traditional method is used to segment the subjective question area of the test paper, which can not effectively segment different types of answer sheets and has the defects of low segmentation accuracy. The edge detection method is used to correct the subjective question area of the answer sheet, and the boundary box of the subjective question part is accurately screened by positioning the top, left, and bottom positioning lines of the answer sheet to complete the segmentation of the subjective question area in marking. The experimental results show that the correct rate of the new method is more than 95%, which is much higher than that of the traditional method. The new method can be widely used in intelligent marking work, and can effectively improve the efficiency of intelligent marking.

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“…The vector space model separates the smallest semantic units such as words and phrases in the text and takes the calculated similarity as vector elements. Teaching cosine is used in two English sentences to obtain semantic similarity [ 6 , 7 ].…”

Section: Introduction To Relevant Technologies Of Intelligent Evaluat...mentioning

confidence: 99%

Intelligent Evaluation Algorithm of English Writing Based on Semantic Analysis

Wang¹,

Liu

2022

Computational Intelligence and Neuroscience

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In order to solve the intelligent evaluation of English writing, this paper proposes a method based on the English semantic neural network algorithm. This paper first briefly analyzes the research background of the English semantic analysis system, then expounds on the relevant technologies of the English distance similarity algorithm, semantic analysis intelligent algorithm structure, word analysis algorithm, sentence part of speech analysis algorithm, sentence semantic analysis algorithm, and neural network algorithm, and finally expounds the database and method implementation of the English semantic analysis system, so as to provide guarantee for the design of the English semantic analysis system. The experimental results show that the recognition accuracy of the BRF network for English characters can reach 96.35%, which is 7.79% higher than that of the BP network; the AUC of the BRF network reaches 0.89, which is closer to 1 compared with 0.72 of the BP network. The test results are in good agreement with the antinoise curve test results of the figure. It is proved that the English semantic neural network algorithm can effectively improve the accuracy of English translation and further improve the efficiency of the system.

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Design of English Intelligent Simulated Paper Marking System

Cited by 3 publications

References 26 publications

An Auxiliary Teaching System for Spoken English Based on Speech Recognition Technology

An Auxiliary Teaching System for Spoken English Based on Speech Recognition Technology

Intelligent segmentation of subjective question area by removing template in intelligent marking

Intelligent Evaluation Algorithm of English Writing Based on Semantic Analysis

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