Ziwei Zhou
Iowa State University, USA
Sowmya Vajjala
National Research Council, Canada
Seyed Vahid Mirnezami
Iowa State University, USA
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Published in: 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, p. 86-92
NEALT Proceedings Series 39:9, p. 86-92
Published: 2019-09-30
ISBN: 978-91-7929-998-9
ISSN: 1650-3686 (print), 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.
