Application of Fuzzy Logic-Based Multi-Feature Audio Classification and Speech Matching in English Speaking Teaching and Learning

As the material foundation of language, speech is the basis for mastering language skills and capturing language information, and English learning must begin with the correct mastery of spoken language. Therefore, spoken language teaching occupies a rather important position in English teaching. In this study, we extract various features such as time-domain features and frequency-domain features from English spoken audio signals, use fuzzy logic inference model to represent each audio feature mapping as an affiliation function, and then optimize the parameters of the affiliation function by using adaptive neuro-fuzzy inference system, and solve the affiliation function to get the result of speech matching by the center of gravity method. Subsequently, a speech evaluation system is designed based on the speech matching model to assist intelligent spoken language teaching. The results of teaching practice show that students in the experimental class using the voice assessment system as a learning aid are significantly better than the control class in terms of speaking skills and learning attitudes (P<0.05). Through real-time feedback and personalized practice, the voice assessment system enables students to correct pronunciation errors immediately and gradually improve their speaking fluency and accuracy. It can also improve students' self-efficacy and learning motivation. This study confirms the effectiveness of the fuzzy logic-based audio classification and speech matching model in improving students' spoken English proficiency and reveals its potential for wide application in future spoken English education.