Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Optimizing Automatic Speech Recognition for Low-Proﬁcient Non-Native Speakers | Hindawi Publishing Corporation EURASIP Journal on Audio Speech and Music Processing Volume 2010 Article ID 973954 13 pages doi 2010 973954 Research Article Optimizing Automatic Speech Recognition for Low-Proficient Non-Native Speakers Joost van Doremalen Catia Cucchiarini and Helmer Strik Department of Language and Speech Radboud University 6500 HD Nijmegen The Netherlands Correspondence should be addressed to Joost van Doremalen Received 1 June 2009 Accepted 5 September 2009 Academic Editor Georg Stemmer Copyright 2010 Joost van Doremalen et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Computer-Assisted Language Learning CALL applications for improving the oral skills of low-proficient learners have to cope with non-native speech that is particularly challenging. Since unconstrained non-native ASR is still problematic a possible solution is to elicit constrained responses from the learners. In this paper we describe experiments aimed at selecting utterances from lists of responses. The first experiment on utterance selection indicates that the decoding process can be improved by optimizing the language model and the acoustic models thus reducing the utterance error rate from 29-26 to 10-8 . Since giving feedback on incorrectly recognized utterances is confusing we verify the correctness of the utterance before providing feedback. The results of the second experiment on utterance verification indicate that combining duration-related features with a likelihood ratio LR yield an equal error rate EER of which is significantly better than the EER for the other measures in isolation. 1. Introduction The increasing demand for innovative applications that support language learning has led to a growing interest in Computer-Assisted Language Learning CALL systems that .