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Experiments on non-native speech assessment and its consistency

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Linkhttps://aclanthology.org/W19-6309
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Affiliation
  1. National Research Council Canada. Digital Technologies
FormatText, Article
Conference8th 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.
Date published
PublisherLiU Electronic Press
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  • Proceedings of the 8th Workshop on NLP for Computer Assisted Language Learning (September 2019): 86–92.
LanguageEnglish
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 identifier55fea725-1b95-4e8f-99b9-8b36534c68f4
Record created2022-01-13
Record modified2022-01-14

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