SCS Undergraduate Thesis Topics
|John Kowalski||Geoff Gordon||Using Machine Learning Techniques to Uncover What Makes Understanding Spoken Chinese Difficult for Non-native Speakers|
The Chinese dictation tutor has been used for the past few years in over thirty classrooms at universities around the world. A large amount of data have been collected from this program on the types of errors students make when trying to spell the pinyin of the Chinese phrase spoken to them. I plan to use this data to help answer the question of what is hard about understanding Chinese. Is it a particular set of consonants, vowels, or tones? Or perhaps do certain difficulties arise in the context in which these sounds are spoken? Since each pinyin phrase can be broken down into features (consonants, vowel sounds, and tones), we can apply machine learning techniques to uncover the most confounding aspects for beginning students of Chinese. We can extend the methods we developed here to create an ML engine that learns on the fly for each student what they find difficult. The items to be presented to the learner can be chosen from a pool that has features with the lowest probability of being correctly classified by the student. This will allow the Chinese learner to focus on what he or she is having most difficulty and hopefully more quickly understand spoken Chinese than without such focused "intelligent" instruction.