Noise-robust and stress-free visualization of pronunciation diversity of World Englishes using a learner's self-centered viewpoint

Abstract

The term of “World Englishes” describes the current and real state of English and one of their main characteristics is a large diversity of pronunciation, called accents. We have developed two techniques of individual-based clustering of the diversity [1, 2] and educationally-effective visualization of the diversity [3]. Accent clustering requires a technique to quantify the accent gap between any speaker pair and visualization requires a technique of stress-free plotting of the speakers. In the above studies, however, we developed and assessed these two techniques independently and in this paper, we assess our technique of automatic accept gap prediction when it is used for our stress-free visualization. Further, since CALL applications today are not always used in a quiet environment, we introduce a feature enhancement (denoising) technique to improve noise-robustness of accent gap prediction. Results show that our accent gap prediction shows correlation of 0.77 to IPA-based manually-defined accent gaps and that, by applying feature enhancement to noisy input utterances, our technique can predict the accent gap that could be obtained in a clean condition, when the SNR is larger than 10 [dB].