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