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Second language (L2) speech is often labeled with the native, phonecategories. However, in many cases, it is difficult to decide on a categoricalphone that an L2 segment belongs to. These segments are regarded asnon-categories. Most existing approaches for Mispronunciation Detection andDiagnosis (MDD) are only concerned with categorical errors, i.e. a phonecategory is inserted, deleted or substituted by another. However,non-categorical errors are not considered. To model these non-categoricalerrors, this work aims at exploring non-categorical patterns to extend thecategorical phone set. We apply a phonetic segment classifier to generatesegmental phonetic posterior-grams (SPPGs) to represent phone segment-levelinformation. And then we explore the non-categories by looking for the SPPGswith more than one peak. Compared with the baseline system, this approachexplores more non-categorical patterns, and also perceptual experimentalresults show that the explored non-categories are more accurate with increasedconfusion degree by 7.3% and 7.5% under two different measures. Finally, wepreliminarily analyze the reason behind those non-categories.

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