Forced-Alignment and Edit-Distance Scoring for Vocabulary Tutoring Applications
We demonstrate an application of Automatic Speech Recognition (ASR) technology to the assessment of young children’s basic English vocabulary. We use a test set of 2935 speech samples manually rated by 3 reviewers to compare several approaches to measuring and classifying the accuracy of the children’s pronunciation of words, including acoustic confidence scoring obtained by forced alignment and edit distance between the expected and actual ASR output. We show that phoneme-level language modeling can be used to obtain good classification results even with a relatively small amount of acoustic training data. The area under the ROC curve of the ASR-based classifier that uses a bi-phone language model interpolated with a general English bi-phone model is 0.80 (95% CI 0.78–0.82). The point where both sensitivity and specificity are at their maximum is where sensitivity is 0.74 and the specificity is 0.80 with 0.77 harmonic mean, which is comparable to human performance (ICC=0.75; absolute agreement = 81%).
KeywordsAutomatic speech recognition vocabulary tutor sub-word language modeling
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