Optimize the Teaching of Chinese Pronunciation as a Foreign Language from the Perspective of Data Science

EasyChair Preprint no. 14142

Abstract

This paper aims to harness smart systems' advantages in reusing language and speech databases to build an AI-assisted trilingual big data platform for Mandarin teaching. It addresses challenges faced by Portuguese-speaking countries in acquiring Mandarin pronunciation and intonation. Methods include learning from Chinese as a foreign language experience, conducting basic training of 400 Chinese characters, pronunciation, and intonation via man-machine mutual assistance; utilizing English and Chinese big data resources to form a trilingual knowledge ontology; exploring excellent teachers' and learning teams' experience to create knowledge modules; mastering pronunciation and intonation training through AI Chinese-mathematics-English tools. The result combines 400 Chinese characters and 800 basic English words, passed through groups to achieve three-language basic equivalents. The significance lies in solving difficulties in teaching Chinese as a foreign language in Portuguese-speaking countries, enhancing proficiency in basic vocabulary, pronunciation, and intonation, and ultimately forming an AI-assisted teaching environment for mastering Putonghua with 2,500 Chinese characters.

Keyphrases: AI-assisted Trilingual Big Data Platform, Man-machine Mutual Assistance in Mandarin Teaching, mastering the most basic vocabulary, Portuguese-speaking countries, Teaching Chinese as a foreign language, the process of acquiring pronunciation and intonation, the relational database of language and speech, three kinds of smart systems

@Booklet{EasyChair:14142,
  author = {Jinping Zhang and Xiaohui Zou},
  title = {Optimize the Teaching of Chinese Pronunciation as a Foreign Language from the Perspective of Data Science},
  howpublished = {EasyChair Preprint no. 14142},

  year = {EasyChair, 2024}}