Experiments on Non-native Speech Assessment and its Consistency

Ziwei Zhou
Iowa State University, USA

Sowmya Vajjala
National Research Council, Canada

Seyed Vahid Mirnezami
Iowa State University, USA

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Ingår i: Proceedings of the 8th Workshop on Natural Language Processing for Computer Assisted Language Learning (NLP4CALL 2019), September 30, Turku Finland

Linköping Electronic Conference Proceedings 164:9, s. 86-92

NEALT Proceedings Series 39:9, p. 86-92

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Publicerad: 2019-09-30

ISBN: 978-91-7929-998-9

ISSN: 1650-3686 (tryckt), 1650-3740 (online)


In this paper, we report some preliminary experiments on automated scoring of non-native English speech and the prompt specific nature of the constructed models. We use ICNALE, a publicly available corpus of non-native speech, as well as a variety of non-proprietary speech and natural language processing (NLP) tools. Our results show that while the best performing model achieves an accuracy of 73% for a 4-way classification task, this performance does not transfer to a cross-prompt evaluation scenario. Our feature selection experiments show that most predictive features are related to the vocabulary aspects of speaking proficiency.


automated language assessment, speech scoring, non-native speech


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