Experiments on non-native speech assessment and its consistency

From National Research Council Canada

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Link	https://aclanthology.org/W19-6309
Author	Search for: Zhou, Ziwei; Search for: Vajjala, Sowmya¹; Search for: Mirnezami, Seyed Vahid
Affiliation	National Research Council Canada. Digital Technologies
Format	Text, Article
Conference	8th Workshop on NLP for Computer Assisted Language Learning
Abstract	In this paper, we report some preliminary experiments on automated scoring of nonnative 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.
Publication date	2019-09
Publisher	LiU Electronic Press
Licence	Creative Commons, Attribution 4.0 Generic (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/
In	Proceedings of the 8th Workshop on NLP for Computer Assisted Language Learning (September 2019): 86–92.
Language	English
NRC publication	This is a non-NRC publication "Non-NRC publications" are publications authored by NRC employees prior to their employment by NRC.
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Record identifier	55fea725-1b95-4e8f-99b9-8b36534c68f4
Record created	2022-01-13
Record modified	2022-01-14

Date modified:: 2026-02-07