Characterizing Phonetic Transformations and Acoustic Differences Across English Dialects.

摘要

In this work, we propose a framework that automatically discovers dialect-specific phonetic rules. These rules characterize when certain phonetic or acoustic transformations occur across dialects. To explicitly characterize these dialectspecific rules, we adapt the conventional hidden Markov model to handle insertion and deletion transformations. The proposed framework is able to convert pronunciation of one dialect to another using learned rules, recognize dialects using learned rules, retrieve dialect-specific regions, and refine linguistic rules. Potential applications of our proposed framework include computerassisted language learning, sociolinguistics, and diagnosis tools for phonological disorders.