Using articulatory representations to detect segmental errors in normative pronunciation

作者：Tepperman Joseph^*; Narayanan Shrikanth

来源：IEEE Transactions on Audio Speech and Language Processing, 2008, 16(1): 8-22.

DOI：10.1109/TASL.2007.909330

• articulatory features
• hidden-articulator Markov model (HAMM)
• language learning
• nonnative speech
• pronunciation evaluation
• reading assessment

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 normative speakers in the ISLE corpus by as much as 16%-17% for some target segments, and a 3%-4% absolute improvement overall.

• 出版日期2008-1