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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. [ABSTRACT FROM AUTHOR]

English translation, also a kind of language conversion, refers to the activities of expressing each other in English to express another language or another language to express English. This paper aimed to evaluate and research methods of English translation based on the improved Gaussian mixture model (GMM). The paper proposed a simple evaluation and analysis of English translation using a discriminative training algorithm. In the experimental part, on the one hand, taking students, teachers, and translators as case study objects, through a questionnaire survey, it can be known that the number of students who scored more than 80 points in the English translation test is increasing every year, from 11.30% in the first year to 21.13% in the fourth year, an increase of nearly 10%. On the other hand, it can be seen from the speech translation experiment that the correct translation rate was 46.18% by using intelligent technology to distinguish translation. The experimental results showed that the discriminative training algorithm under the improved GMM algorithm is effective in the research of English translation evaluation. [ABSTRACT FROM AUTHOR]

Traditional English classroom teaching is difficult to meet the oral learning needs of most learners. Thanks to the continuous advancement of speech processing technology, computer-assisted language learning systems are becoming more intelligent, not only pointing out learners' pronunciation errors but also assessing their overall pronunciation level. Method. This paper uses the method of tree kernel function to measure the similarity of two ontology trees. According to the features of nodes in ontology tree, methods to calculate the external features and internal features of nodes are proposed, respectively. External features are mainly obtained by calculating the hierarchical centrality, node density, and node coverage of nodes in the ontology tree; internal features are mainly obtained by measuring the richness of internal information. According to the similarity of ontology tree and the external features and internal features of nodes, the calculation formula of structural comprehensive similarity is improved, and the features of ontology itself can be fully considered in the calculation. According to the difference of the structure, the weights of the corresponding features during the calculation are adjusted autonomously, so that the calculation results are closer to reality. In spectral image preprocessing, endpoint detection utilizes the harmonic characteristics presented by narrowband spectrograms with high frequency resolution and eliminates useless nonspeech segments by detecting the presence of voiced segments. When building the neural network model, four convolutional layers, two fully connected layers, and one softmax output layer were conceived, and dropout was used to randomly suspend the work of some neurons to avoid overfitting. Results/Discussion. Through the data analysis of mean and variance and verified by one-way analysis of variance, it proves that the sentiment evaluation method in this paper is effective. The traditional multiple linear regression method is not suitable for the corpus and application scenarios of this paper. This paper proposes a decision tree structure, which is similar to the overall scoring process of raters, and uses the Interactive Dicremiser version 3 (ID3) algorithm to build a comprehensive evaluation decision tree for pitch, rhythm, intonation, speech rate, and emotion indicators. It is proved by experiments that the accurate consistency rate of the human-machine evaluation in this paper is 93%, the adjacent consistency rate is 96%, and the Pearson correlation coefficient value of the human-machine evaluation results is 0.89. The data results prove that the evaluation method in this paper is credible. [ABSTRACT FROM AUTHOR]

Traditional English teaching focuses on exam-oriented education. Although many students have passed various English tests and even achieved high scores, they cannot communicate freely in English. In order to change this situation, the state has adjusted the syllabus to enhance students' audiovisual (AV) and oral ability. With more attention paid to the course of audiovisual speaking, the improvement of teaching quality has become an important part. The rationality of teaching effect evaluation plays a key role. Therefore, it is necessary to establish a scientific and reasonable AV and oral teaching effect evaluation model. Based on this, this paper proposes an evaluation algorithm of English AV and oral teaching effect based on deep learning (DL). Its characteristics are as follows: firstly, a data-based objective evaluation method of teaching effect is constructed, and then the adaptive algorithm is used to optimize the initial weight and threshold of deep neural network. English teaching implementation shows to us that English language characteristic and the matter of actual activities are closely connected to the context. English learning should not only pay attention to the understanding of English vocabulary and sentence and also carry out cultural exchanges with the countries, but English is the common language. The experimental results show that this model improves the prediction accuracy and convergence speed of teaching quality evaluation results, providing a more feasible scheme for the evaluation of English AV teaching effect. [ABSTRACT FROM AUTHOR]