We are exploiting our manually annotated corpus to design more robust forced speech-text alignment algorithms by anticipating possible errors made by learners.
the non-native realizations are studied in order to investigate efficient acoustic feedback provided to learners, and how feedback can robustly interact with automatic segmentation provided by automatic speech recognition.