Paper: Syllable and language model based features for detecting non-scorable tests in spoken language proficiency assessment applications

ACL ID W14-1811
Title Syllable and language model based features for detecting non-scorable tests in spoken language proficiency assessment applications
Venue Innovative Use of NLP for Building Educational Applications
Session
Year 2014
Authors

This work introduces new methods for de- tecting non-scorable tests, i.e., tests that cannot be accurately scored automatically, in educational applications of spoken lan- guage proficiency assessment. Those in- clude cases of unreliable automatic speech recognition (ASR), often because of noisy, off-topic, foreign or unintelligible speech. We examine features that estimate signal- derived syllable information and compare it with ASR results in order to detect responses with problematic recognition. Further, we explore the usefulness of lan- guage model based features, both for lan- guage models that are highly constrained to the spoken task, and for task inde- pendent phoneme language models. We validate our methods on a challenging dataset of young English language learn- ers (ELLs) inte...