Audio and Visual Exaggerated Expressive Speech Generation of English Language Learning Based on Automatic Context Algorithm - Huang, Jie; Gong, Xun (International Wireless Communications and Mobile Computing Conference (Online), 2021)
In the past so many years, English linguists have conducted extensive research on teachers' teaching in English education, and they have also begun to be interested in how students learn to learn English language in aural and visual exaggerated expressive voice generation. Chinese linguists have made considerable progress in the study of English language learning strategies, and found many progressing studies. However, few studies have focused on the speech generation strategies of auditory and visual exaggerated expressiveness in learning English language learning. So far, no research has been conducted on the correlation between auditory and visual exaggerated expressive speech generation in English language learning. Therefore, based on this, the purpose of this paper is to study the auditory and visual exaggerated speech generation of English language learning based on automatic context algorithms. This algorithm can provide a useful reference for the development of English language learning models in my country. Based on relevant knowledge and practical theories, this paper provides the development of auditory and visual exaggerated expressive speech generation for English language learning based on the optimization design of automatic context algorithm.