Towards automatic scoring of non-native spontaneous speech
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Automatic assessment of spoken modern standard Arabic
EdAppsNLP '09 Proceedings of the Fourth Workshop on Innovative Use of NLP for Building Educational Applications
Elicited imitation for prediction of OPI test scores
IUNLPBEA '11 Proceedings of the 6th Workshop on Innovative Use of NLP for Building Educational Applications
Exploring content features for automated speech scoring
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Using an ontology for improved automated content scoring of spontaneous non-native speech
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
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This paper describes a system aimed at automatically scoring two task types of high and medium-high linguistic entropy from a spoken English test with a total of six widely differing task types. We describe the speech recognizer used for this system and its acoustic model and language model adaptation; the speech features computed based on the recognition output; and finally the scoring models based on multiple regression and classification trees. For both tasks, agreement measures between machine and human scores (correlation, kappa) are close to or reach inter-human agreements.