Shu Gong

Work place: USTC iFlytek Speech Laboratory, University of Science and Technology of China, Hefei, China



Research Interests: Error Control, Detection Theory


Shu Gong was born in Hefei, China in 1983. He is an engineer working at ZTE (Zhongxing Telecom Equipment) and received his master’s degree on speech signal processing in 2010.

During the master study, he mainly research on computer assisted pronunciation quality
assessment and automatic error detection. He introduced discriminative training and TANDEM features into automatic PSC evaluation system. He also helped to develop the system for Germans learning Chinese. 

Author Articles
Pronunciation Proficiency Evaluation based on Discriminatively Refined Acoustic Models

By Ke Yan Shu Gong

DOI:, Pub. Date: 8 Mar. 2011

The popular MLE (Maximum Likelihood Estimation) is a generative approach for acoustic modeling and ignores the information of other phones during training stage. Therefore, the MLE-trained acoustic models are confusable and unable to distinguish confusing phones well. This paper introduces discriminative measures of minimum phone/word error (MPE/MWE) to refine acoustic models to deal with the problem. Experiments on the database of 498 people’s live Putonghua test indicate that: 1) Refined acoustic models are more distinguishable than conventional MLE ones; 2) Even though training and test are mismatch, they still perform significantly better than MLE ones in pronunciation proficiency evaluation. The final performance has approximately 4.5% relative improvement.

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