Abstract
This paper provides a comprehensive explanation of the theoretical foundations of multimodal discourse analysis theory as applied to speaking instructional design. The specific application of multimodal theory in the teaching of elementary English speaking classrooms is explored through the teaching design of elementary English speaking classrooms, the teaching implementation of multimodal teaching design is carried out, and the effect of the teaching practice of elementary English speaking guided by multimodal discourse analysis theory is comprehensively evaluated through classroom observation method, questionnaire survey method, and interview method, combined with the teaching evaluation and teaching implementation effect, which is the multimodal teaching design. The paper also summarizes the findings and shortcomings of the study. Through the teaching design and implementation, the advantages of multimodal teaching are obvious; it can combine with modern advanced teaching techniques to create more realistic communicative situations in the classroom, gather and present various modal resources and information, and ensure rich and diverse language input; students can receive various sensory stimuli in the classroom, deepen their memory and experience of language, increase the interest of classroom teaching, and improve students’ participation. It also increases the interest of the classroom and enhances students’ participation and motivation. Based on multimodal theory, the author designed a multimodal teaching framework for a semester‐long speaking course in the speaking classroom for reference. The fuzzy measures were constructed based on subsets of language segments containing 10 phonemes belonging to the same HDP set. Finally, linguistic scores are given by the Surgeon integral model based on the plausibility of the system and the fuzzy measures. The experimental results based on Sphinx‐4 show that the evaluation model yields plausible and stable evaluation results for the 3 test sets at an average correct recognition rate of 84.7% of phonemes.
Highlights
In many real-life scenarios, there is a need to assess the speaker’s oral expression ability, such as Mandarin exams, oral training, language teaching evaluation, and broadcasting exams
In the interactive sentiment recognition parameter optimization method, scalars and model graphs are visualized by the tensor board tool, and a judgment mechanism is used to empirically judge these visual graphs to achieve timely adjustment of hyperparameters and optimization of the model framework
The overall scoring efficiency is low; for language learners, there are many hidden oral expression problems in the learning process that cannot be discovered in time, which affects the efficiency of language learning
Summary
In many real-life scenarios, there is a need to assess the speaker’s oral expression ability, such as Mandarin exams, oral training, language teaching evaluation, and broadcasting exams. The study of spoken English evaluation algorithms based on key technologies such as speech recognition and natural language processing provides the ability to interact with the user’s spoken language, automatic evaluation of the user’s spoken language, and the ability to allow the user to automatically discover the crux of his or her lack of fluency [3] The implementation of this feature will greatly enhance the efficiency of English-speaking learning, promote the maturity of Computer Assisted Language Learning (CALL) technology, solve the current problem of insufficient English-speaking teachers, and promote the development of natural language processing which is important. The system assists in oral training and helps language learners to learn anytime and anywhere and assists scorers in automatic scoring This approach gives an objective and fair assessment and ensures relative fairness among participants and saves a lot of costs and improves overall efficiency. For the easy-mix evaluation algorithm, the Surgeon integral was introduced to obtain a linguistic score
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