Classification Of Chinese Dialect Regions From L2 English Speech

This paper presents an effort to classify Chinese speakers' L1 dialect regions from their L2 English speech. By applying LightGBM (a gradient boosting classifier based on decision trees) to softmax-based features from deep neural networks, our system achieved 68% accuracy on five dialect regions using one sentence and 82% accuracy using 23 words. The results represent a nearly 50% error reduction over a baseline system based on HMM/GMM and forced alignment. We demonstrated that modeling phone boundaries and vowel stress yielded a relative error reduction of 18%, with phone boundaries being more useful than vowels and consonants. Furthermore, in terms of classification models, LightGBM was extremely robust on this task, which we believe deserves further investigation.