Improved and robust prediction of pronunciation distance for individual-basis clustering of World Englishes pronunciation

摘要

English is the only language available for global communication and is used by approximately 1.5 billions of speakers. It is also known to have a large diversity of pronunciation due to the influence of speakers' mother tongue, called accents. Our project aims at creating a global and individual-basis map of English pronunciations to be used in teaching and learning World Englishes (WE) as well as research studies of WE [1, 2]. Creating the map mathematically requires a distance matrix in terms of pronunciation differences among all the speakers considered, and technically requires a method of predicting the pronunciation distance between any pair of the speakers only by using their speech samples. In our previous study [3], we combined invariant pronunciation structure analysis [4, 5, 6, 7] and Support Vector Regression (SVR) to predict the inter-speaker pronunciation distances. In this paper, several techniques are introduced and examined whether they can increase accuracy and robustness of prediction. Experiments show that the correlation between IPA-based reference distances and the predicted distances is increased from 0.805 to 0.903, which is over the correlation of 0.829 that is obtained by using the phoneme-based ground truth distances.