Exploring the Teaching Mode of English Audiovisual Speaking in Multimedia Network Environment

Abstract

Introducing multimedia network tools in English audiovisual teaching and building a new model of network-based multimedia teaching can make English audiovisual teaching more in line with students’ cognitive thinking characteristics and processes. This can improve the overall efficiency of English teaching in schools. Computers have been widely used in language evaluation and speech recognition for language learning, and speech recognition technology is an important reflection of the level of language learning. The large amount of language signal data, complex pronunciation changes, and high dimensionality of pronunciation feature parameters in the language learning process make it difficult to identify pronunciation features. The computational volume of pronunciation evaluation and recognition is too large, which requires high hardware resources and software resources to realize high-speed processing of massive pronunciation signals. To address the problem of low recognition rate of English pronunciation, this study proposes a sound recognition algorithm based on adaptive particle swarm optimization (PSO) matching pursuit (MP) sparse decomposition. The algorithm firstly improves the parameter adaptive setting of PSO based on the particle and population evolution rate, establishes parameter adaptive PSO, and realizes the optimization of adaptive PSO optimized MP sparse decomposition. The continuous Gabor super-complete atomic set is constructed based on the continuous space search property of PSO to improve the optimal atomic matching of the evolutionary process. Finally, the recognition of English pronunciation is realized by the support vector machine (SVM) algorithm. The test results show that the misjudgement rate for different mispronunciations is less than 1% when the system is used to evaluate the English pronunciation level. It proves that the method can effectively detect the mispronunciation and has high evaluation accuracy.