A Teaching Mode of College English Listening in Intelligent Phonetic Environments

A Teaching Mode of College English Listening in Intelligent Phonetic Environments

Xin Yan
Copyright: © 2024 |Pages: 17
DOI: 10.4018/IJeC.347986
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Abstract

This paper discusses the integration of cutting-edge technologies, especially artificial intelligence (AI) and speech synthesis in UETL environment. By using methods based on artificial intelligence, such as Fuzzy Convolutional Neural Network (FCNN) and Improved Hidden Markov Model (MHMM), this study aims to reform the traditional teaching paradigm. Through the in-depth study of the experiment, it illustrates how these innovations can enhance students' autonomous learning, understanding and participation in English language education. The implementation of speech synthesis mechanism realizes the conversion from real-time speech to text, and promotes interactive learning experience and personalized feedback. The comparative analysis before and after adopting advanced teaching methods shows that students' learning achievements and the overall effectiveness of UETL process have been significantly improved. This study emphasizes the revolutionary potential of integrating artificial intelligence and speech synthesis technology to optimize college English education.
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With the improvement of information quality and the development of AI, researchers, suppliers, and authorities have created new potential and serious difficulties in the school environment. Fifteen portable information management samples from the school system were selected as the prototypes for constructing a parent reference operating system (Abdullah et al., 2019). In order to effectively predict athletes' sports performance and provide a reliable analysis framework for the establishment of physical education teaching objectives, Abdullah and others developed a technology to predict college students' sports performance by combining neural network with particle swarm optimization.

The particle swarm optimization (PSO) algorithm updates the particle direction and velocity through independent absolute values and the maximum and minimum values of global data points, optimizes the variability and intensity training of the neural network, and improves the accuracy of the neural network in predicting college students' sports performance (Xu et al., 2021). The teaching methods proposed by Xu et al. are a feasible solution to this problem, because they have been proved to effectively support students to choose attractive and important demonstration components that meet their educational goals (Predić et al., 2024).

In response, the researchers developed a short lecture content recommendation system, which was incorporated into the e-book reading system as part of educational methods (Yang et al., 2021). This method assists students in locating pages that contain crucial material that must be fully involved prior to class. The recommended system was built on the foundation of our previous work. The focus of this research is to examine the advantages and disadvantages of using the flipped classroom in conjunction with case- and team-based learning (FC-CTBL) for residency training. A total of 60 Xiangya Medical College younger surgery trainees participated in this investigation. The topic of this investigation was “diabetic foot” (Ding et al., 2021). In the whole research process, the semi-experiment is carried out in a student-centered postgraduate project. Participants in the experimental group used only this method for research, while students in the control group could not use other functions.

The development of digital training benefits from the progress of network technology. In higher education, virtual education technologies such as e-book reading systems are usually used to help students reflect and plan their study. Although many studies have explored the relationship between students' reading habits and their academic performance, few researchers have studied the effects of self-regulated learning, learning strategies and self-efficacy on students' academic performance (Yang et al., 2020).

Automatic identification tools can help radiologists prioritize their work lists by marking suspicious or positive examples for early evaluation. Artificial intelligence systems can extract “radioactivity” information from photos that is invisible to the naked eye (Thrall et al., 2018), which may improve the prediction and treatment effect of image sets (Huisman et al., 2021). Another method is to use a personal system to manage classroom activities by implementing an innovative plan, which adopts the advantages of information technology and uses a valuable method to check the sacrifices made by teachers and students for implementing the plan (Zhang et al., 2019). The new method is realized by speech synthesis, because this part needs speech enhancement. In order to synthesize text sequences (Yang et al., 2021), more attention is paid to features related to sound, which only focus on the whole world (Yang et al., 2020).

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