Abstract
This thesis describes an automatic pronunciation scoring framework using learning to rank and class-normalized, dynamic-programming-based quantization. The goal is to train a model that is able to grade the pronunciation of a second language learner, such that the predicted score is as close as possible to the one given by a human teacher. Under this framework, each utterance is given a score of 1 to 5 by human raters, which is treated as a ground truth rank for the training algorithm. The corpus was rated by qualified English teachers in Taiwan (nonnative speakers). Nine phone-level scores are computed and converted into word-level scores through four conversion methods. We select the 16 best performing scores as the input features to train the learning-to-rank function. The output of the function is then quantized to a discrete rank on a 1-5 scale. The quantization is done with class normalization to alleviate the problem of data imbalance over different classes. Experimental results show that the proposed framework achieves a higher correlation to the human scores than other methods, along with higher accuracy in detecting instances of mispronunciation. We also release a new version of our nonnative corpus with human rankings.