Acoustic Model Refining Algorithm for Pronunciation Quality Evaluation
Li-Rong Dai · Zhongwen xinxi xuebao · 2013
Traditional approach uses only the standard-pronounced speech data to build acoustic models,which makes automatic pronunciation systems poor show for accented speech data since the training and test are mismatch.To deal with the problem,this paper presents a novel algorithm that utilizes both standard and accented speech data to optimize acoustic model by minimizing the root mean square error between the manual and the machine scores.Experiments on 3 685 live Putonghua database(498 for test and 3 187 for training) shows that the evaluation acoustic models generated by the proposed method are significantly better than those by traditional approaches.