Machine translation and speech recognition are major enabling technologies to innovation and application in the language service industry. The current research develops the two methods individually and suggests a DR-Reformer multilingual translation technique based on optimal transportation and a Speech Recognition solution based on TDNN-LSTM to be used in Chinese. Based on this premise, both approaches are included in the reorganization of the Chinese teaching content framework and will define a new way of combining intelligent language services with educational content. To be more precise, the optimized DR-Reformer method is used to facilitate the understanding of language and reduce the level of complexity in learning Chinese, whereas the TDNN-LSTM speech recognition method is introduced in order to create a non-invasive educational setting and improve the quality of education. Each of the two methods is evaluated independently, followed by the implementation of the integrated approach in the teaching practice. The outcomes reveal that the experimental class that adopted this approach had an average of 75.84 12.253 and 86.92 8.074 in midterms and finals respectively, which were 5.55 and 9.36 higher than their counterparts who underwent traditional teaching. The results indicate that the instructional structure, which is backed by multilingual translation and TDNN-LSTM-based speech processing, is workable, efficient, and better than the traditional version, providing valuable directions to Chinese teaching practice.
