Abstract
Pursuing Chinese and Uygur bilingual teaching in XinJiang Province is an important government policy for improving communication between different language speakers. An automatic assessment system can yield many advantages in such bilingual teaching. However, as an agglutinative language, Uygurs particular way of word-building results in leaving many words out of the lexicon. Therefore, performance of the old pronunciation assessment system, which is based on a traditional ASR system, is poor. For building a high-performance system, we decide to use subwords as basic recognition units after analyzing the rules and habits of Uygur pronunciation. Experimental results indicate that the accuracy of the subword-based system is greatly improved by the implementation of machine segmentation based on double-level morphology analysis of Uygur subwords, calculating the posterior probabilitys denominator with a phoneme decoder, and calculating confidence at the subword level.
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