Statistical phone duration modeling to filter for intact utterances in a computer-assisted pronunciation training system

We study the use of a statistical phone duration model for separating intact utterances from corrupted ones in a computer-assisted pronunciation training system. Our system performs forced alignment between the input utterance and the canonical transcription of the prompted text. Intact utterances contain spoken content that correspond to the text prompt. For these utterances, our system performs detailed phonetic analysis of the alignment and generates corrective feedback to highlight the occurrence of phonetic errors. Corrupted utterances result from disfluencies, truncated recordings, or spoken content that does not correspond to the text prompt. For these cases, the appropriate feedback is to invite the user to record again. We develop a filtering mechanism for intact input utterances by means of phone duration modeling. The likelihood-ratiotest involving the phone-specific duration probability and an antimodel probability gave the best EER of 17.16%, which is a 20% relative improvement over the baseline approach that incorporates phone-posterior probabilities.