Pronunciation Error Detection for Computer Assisted Pronunciation Teaching in Mandarin

In this paper, we provided a strategy of error detection of pronunciation and applied it to the computer-assisted pronunciation teaching(CAPT), especially in Mandarin language learning. In our system, it can be divided into two parts: the sentence verification(SV) and syllable identification(SI). First was used to ban out-of-task sentences. We used the likelihood ratio test, which was computed between the maximum probability of a result under two different hypotheses, i.e. null hypothesis and alternative hypothesis models, to verify the deviation degree and decide whether the student pronunciation is out-of-task. In SV part, the experimental results was significant and had 91.0% rate of F-score. The second part was applied to recognize the content of speech read by the speaker. The recognition net was built as a sausage shape with pronunciation confusion table corresponding to confusion error patterns. Then, the system could find out the wrong pronounced syllable for the appropriate feedback to correct the pronunciation of the users. In the stage of SI, the best detection rate had a F-score rate of 77.2%.