An automatic grading system for spoken English retelling test is presented in this paper. Speech recognition technology is used in the system to evaluate the quality of retelling according to the pre-defined scoring rubric which includes speech fluency, pronunciation accuracy and content integrity. Scoring features for these quality aspects are firstly extracted by applying LVCSR, keyword spotting, forced alignment and confidence measurements. And then, these features are mapped to a score by using SVM model which is pre-trained on human rated test items. According to the experimental results the correlation coefficient between machine scores and expert scores is 0.729, which means that the system can be used in real examination to replace human scores. This work is partially supported by the National Natural Science Foundation of China (No. 10925419, 90920302, 10874203, 60875014, 61072124, 11074275, 11161140319).
In order to give an accurate assessment, the test speech should be recognized firstly in the text-independent pronunciation quality assessment system. Field test data has some flaws which degrade the recognition performance, such as noise, accent and spontaneous speaking style. In this paper, we investigate these factors by improving the acoustic model (AM) for the speech recognition system. Background noise is added to the training data to enhance the ability of anti-noise. Speaker-based Cepstral Mean and Variance Normalization (SCMVN) is adopted to alleviate the distortion of channel and the impact of inter-speaker pronunciation variability. Maximum a Posteriori (MAP) adaptation is applied twice, in order to tune acoustic model to match the pronunciation characteristic of the accent and the spontaneous style in spoken language. According to the experimental results, above measures increase the word correct rate relatively by 44.1% and the correlation coefficient between machine score and expert score relatively by 6.3%.
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