Deep segmental phonetic posterior-grams based discovery of non-categories in L2 English speech

• URL: http://arxiv.org/abs/2002.00205v1
• Date: Sat, 1 Feb 2020 13:21:33 GMT
• Title: Deep segmental phonetic posterior-grams based discovery of non-categories in L2 English speech
• Authors: Xu Li, Xixin Wu, Xunying Liu, Helen Meng
• Abstract summary: Second language (L2) speech is often labeled with the native, phone categories. In many cases, it is difficult to decide on a categorical phone that an L2 segment to.
• Score: 65.04834943405673
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Second language (L2) speech is often labeled with the native, phone categories. However, in many cases, it is difficult to decide on a categorical phone that an L2 segment belongs to. These segments are regarded as non-categories. Most existing approaches for Mispronunciation Detection and Diagnosis (MDD) are only concerned with categorical errors, i.e. a phone category is inserted, deleted or substituted by another. However, non-categorical errors are not considered. To model these non-categorical errors, this work aims at exploring non-categorical patterns to extend the categorical phone set. We apply a phonetic segment classifier to generate segmental phonetic posterior-grams (SPPGs) to represent phone segment-level information. And then we explore the non-categories by looking for the SPPGs with more than one peak. Compared with the baseline system, this approach explores more non-categorical patterns, and also perceptual experimental results show that the explored non-categories are more accurate with increased confusion degree by 7.3% and 7.5% under two different measures. Finally, we preliminarily analyze the reason behind those non-categories.

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