Abstract
Suprasegmental features have received growing attention in the field of oral assessment. In this article we describe a set of computer algorithms that automatically scores the oral proficiency of non-native speakers using unconstrained English speech. The algorithms employ machine learning and 11 suprasegmental measures divided into four groups (prominence, filled pause, speech rate, and intonation) to calculate the proficiency scores. In test responses from 120 non-native speakers of English monologues from the Cambridge English Language Assessment (CELA), the Pearson’s correlation between the computer’s calculated proficiency levels and the official CELA proficiency levels was 0.718. The current findings provide empirical evidence that prominence and intonation are salient features in the computer model’s prediction of proficiency.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1-19 |
| Number of pages | 19 |
| Journal | Language Assessment Quarterly |
| DOIs | |
| State | Accepted/In press - Mar 23 2018 |
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ASJC Scopus subject areas
- Language and Linguistics
- Linguistics and Language
Cite this
The roles of suprasegmental features in predicting English oral proficiency with an automated system. / Kang, Okim; Johnson, David.
In: Language Assessment Quarterly, 23.03.2018, p. 1-19.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - The roles of suprasegmental features in predicting English oral proficiency with an automated system
AU - Kang, Okim
AU - Johnson, David
PY - 2018/3/23
Y1 - 2018/3/23
N2 - Suprasegmental features have received growing attention in the field of oral assessment. In this article we describe a set of computer algorithms that automatically scores the oral proficiency of non-native speakers using unconstrained English speech. The algorithms employ machine learning and 11 suprasegmental measures divided into four groups (prominence, filled pause, speech rate, and intonation) to calculate the proficiency scores. In test responses from 120 non-native speakers of English monologues from the Cambridge English Language Assessment (CELA), the Pearson’s correlation between the computer’s calculated proficiency levels and the official CELA proficiency levels was 0.718. The current findings provide empirical evidence that prominence and intonation are salient features in the computer model’s prediction of proficiency.
AB - Suprasegmental features have received growing attention in the field of oral assessment. In this article we describe a set of computer algorithms that automatically scores the oral proficiency of non-native speakers using unconstrained English speech. The algorithms employ machine learning and 11 suprasegmental measures divided into four groups (prominence, filled pause, speech rate, and intonation) to calculate the proficiency scores. In test responses from 120 non-native speakers of English monologues from the Cambridge English Language Assessment (CELA), the Pearson’s correlation between the computer’s calculated proficiency levels and the official CELA proficiency levels was 0.718. The current findings provide empirical evidence that prominence and intonation are salient features in the computer model’s prediction of proficiency.
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UR - http://www.scopus.com/inward/citedby.url?scp=85044313318&partnerID=8YFLogxK
U2 - 10.1080/15434303.2018.1451531
DO - 10.1080/15434303.2018.1451531
M3 - Article
SP - 1
EP - 19
JO - Language Assessment Quarterly
JF - Language Assessment Quarterly
SN - 1543-4303
ER -