We are developing a Computer Assisted Language Learning (CALL) system for practicing oral proficiency that makes use of Automatic Speech Recognition (ASR) to provide feedback on grammar and pronunciation. Since good quality unconstrained non-native ASR is not yet feasible, we use an approach in which we try to elicit constrained responses. The task in the current experiments is to select utterances from a list of responses. The results of our experiments show that significant improvements can be obtained by optimizing the language model and the acoustic models, thus reducing the utterance error rate from 29–26% to 10–8%.
Bibliographic reference. Doremalen, Joost van / Strik, Helmer / Cucchiarini, Catia (2009): "Optimizing non-native speech recognition for CALL applications", In INTERSPEECH-2009, 592-595.