@inproceedings{lonsdale-christensen-2014-combining,
title = "Combining elicited imitation and fluency features for oral proficiency measurement",
author = "Lonsdale, Deryle and
Christensen, Carl",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Declerck, Thierry and
Loftsson, Hrafn and
Maegaard, Bente and
Mariani, Joseph and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)",
month = may,
year = "2014",
address = "Reykjavik, Iceland",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2014/pdf/1057_Paper.pdf",
pages = "1956--1961",
abstract = "The automatic grading of oral language tests has been the subject of much research in recent years. Several obstacles lie in the way of achieving this goal. Recent work suggests a testing technique called elicited imitation (EI) that can serve to accurately approximate global oral proficiency. This testing methodology, however, does not incorporate some fundamental aspects of language, such as fluency. Other work has suggested another testing technique, simulated speech (SS), as a supplement or an alternative to EI that can provide automated fluency metrics. In this work, we investigate a combination of fluency features extracted from SS tests and EI test scores as a means to more accurately predict oral language proficiency. Using machine learning and statistical modeling, we identify which features automatically extracted from SS tests best predicted hand-scored SS test results, and demonstrate the benefit of adding EI scores to these models. Results indicate that the combination of EI and fluency features do indeed more effectively predict hand-scored SS test scores. We finally discuss implications of this work for future automated oral testing scenarios.",
}
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%0 Conference Proceedings
%T Combining elicited imitation and fluency features for oral proficiency measurement
%A Lonsdale, Deryle
%A Christensen, Carl
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Declerck, Thierry
%Y Loftsson, Hrafn
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14)
%D 2014
%8 May
%I European Language Resources Association (ELRA)
%C Reykjavik, Iceland
%F lonsdale-christensen-2014-combining
%X The automatic grading of oral language tests has been the subject of much research in recent years. Several obstacles lie in the way of achieving this goal. Recent work suggests a testing technique called elicited imitation (EI) that can serve to accurately approximate global oral proficiency. This testing methodology, however, does not incorporate some fundamental aspects of language, such as fluency. Other work has suggested another testing technique, simulated speech (SS), as a supplement or an alternative to EI that can provide automated fluency metrics. In this work, we investigate a combination of fluency features extracted from SS tests and EI test scores as a means to more accurately predict oral language proficiency. Using machine learning and statistical modeling, we identify which features automatically extracted from SS tests best predicted hand-scored SS test results, and demonstrate the benefit of adding EI scores to these models. Results indicate that the combination of EI and fluency features do indeed more effectively predict hand-scored SS test scores. We finally discuss implications of this work for future automated oral testing scenarios.
%U http://www.lrec-conf.org/proceedings/lrec2014/pdf/1057_Paper.pdf
%P 1956-1961
Markdown (Informal)
[Combining elicited imitation and fluency features for oral proficiency measurement](http://www.lrec-conf.org/proceedings/lrec2014/pdf/1057_Paper.pdf) (Lonsdale & Christensen, LREC 2014)
ACL