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

EasyChair Preprint 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, Portuguese-speaking countries, Teaching Chinese as a foreign language, mastering the most basic vocabulary, 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 14142},
  year      = {EasyChair, 2024}}