An Automatic Assessment Method for Spoken English Based on Multimodal Feature Fusion - Qijing Zhang (Hindawi-Wiley, 2021)
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.