Using Articulatory Representations to Detect Segmental Errors in Nonnative Pronunciation

Abstract--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 rep...

• Type: Journal
• Year: 2008
• Where: TASLP
• Authors: Joseph Tepperman, Shrikanth Narayanan
• Keywords: Articulatory Information, Direct Articulatory Measurement, Meaningful Articulatory Representations, TASLP 2008