Using Articulatory Representations to Detect Segmental Errors in Nonnative Pronunciation
Motivated by potential applications in second-language pedagogy, we present a novel approach to using articulatory information to improve automatic detection of typical phone-level errors made by nonnative speakers of English-a difficult task that involves discrimination between close pronunciations. We describe a reformulation of the hidden-articulator Markov model (HAMM) framework that is appropriate for the pronunciation evaluation domain. Model training requires no direct articulatory measurement, but rather involves a constrained and interpolated mapping from phone-level transcriptions to a set of physically and numerically meaningful articulatory representations. Here, we define two new methods of deriving articulatory-based features for classification: one, by concatenating articulatory recognition results over eight streams representative of the vocal tract's constituents; the other, by calculating multidimensional articulatory confidence scores within these representations based on general linguistic knowledge of articulatory variants. After adding these articulatory features to traditional phone-level confidence scores, our results demonstrate absolute reductions in combined error rates for verification of segment-level pronunciations produced by nonnative speakers in the ISLE corpus by as much as 16%-17% for some target segments, and a 3%-4% absolute improvement overall.