With the advancement of globalization, an increasing number of people are learning and using a common language as a tool for international communication. However, there are clear distinctions between the native language and target language, especially in pronunciation, and the domestic target language, the learning environment is far from ideal, with few competent teachers. In addition, such learning cannot achieve computer-assisted language learning (CALL) technology. The efficient combination of computer technology and language teaching and learning methods provides a new solution to this problem. The core of CALL is speech recognition (SR) technology and speech evaluation technology. The development of deep learning (DL) has greatly promoted the development of speech recognition. The pronunciation resource collected from the Chinese college students, whose majors are language education or who are planning to obtain better pronunciation, shall be the research object of this paper. The study applies deep learning to the standard but of target language pronunciation and builds a standard evaluation model of pronunciation teaching based on the deep belief network (DBN). On this basis, this work improves the traditional pronunciation quality evaluation method, comprehensively considers intonation, speaking speed, rhythm, intonation, and other multi-parameter indicators and their weights, and establishes a reasonable and efficient pronunciation model. The systematic research results show that this article has theoretical and practical value in the field of phonetics education.

In recent years, neural network-based English-Chinese translation models have gradually supplanted traditional translation methods. The neural translation model primarily models the entire translation process using the “encoder-attention-decoder” structure. Simultaneously, grammar knowledge is essential for translation, as it aids in the grammatical representation of word sequences and reduces grammatical errors. The focus of this article is on two major studies on attention mechanisms and grammatical knowledge, which will be used to carry out the following two studies. Firstly, in view of the existing neural network structure to build translation model caused by long distance dependent on long-distance information lost in the delivery, leading to problems in terms of the translation effect which is not ideal, put forward a kind of embedded attention long short-term memory (LSTM) network translation model. Secondly, in view of the lack of grammatical prior knowledge in translation models, a method is proposed to integrate grammatical information into translation models as prior knowledge. Finally, the proposed model is simulated on the IWSLT2019 dataset. The results show that the proposed model has a better representation of source language context information than the existing translation model based on the standard LSTM model.