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Atypical Inputs in Educational Applications.

, , , , , and . NAACL-HLT (3), page 60-67. Association for Computational Linguistics, (2018)

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Expert and crowdsourced annotation of pronunciation errors for automatic scoring systems., , , , and . INTERSPEECH, page 2809-2813. ISCA, (2015)Automatic scoring of non-native spontaneous speech in tests of spoken English., , , and . Speech Commun., 51 (10): 883-895 (2009)SpeechraterTM: a construct-driven approach to scoring spontaneous non-native speech., , and . SLaTE, page 128-131. Carnegie Mellon University / ISCA, (2007)Automatic Detection of Off-Topic Spoken Responses Using Very Deep Convolutional Neural Networks., , , , and . INTERSPEECH, page 4200-4204. ISCA, (2019)Applying Rhythm Features to Automatically Assess Non-Native Speech., and . INTERSPEECH, page 1861-1864. ISCA, (2011)Development of Robust Automated Scoring Models Using Adversarial Input for Oral Proficiency Assessment., , , and . INTERSPEECH, page 1871-1875. ISCA, (2019)Efficient Optimization for Bilingual Sentence Alignment Based on Linear Regression., , , and . ParallelTexts@NAACL-HLT, (2003)Exploring Content Features for Automated Speech Scoring., , and . HLT-NAACL, page 103-111. The Association for Computational Linguistics, (2012)Towards Automatic Scoring of Non-Native Spontaneous Speech., and . HLT-NAACL, The Association for Computational Linguistics, (2006)Automated Content Scoring of Spoken Responses in an Assessment for Teachers of English., and . BEA@NAACL-HLT, page 73-81. The Association for Computer Linguistics, (2013)978-1-937284-47-3.