The roles of suprasegmental features in predicting English oral proficiency with an automated system

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.

Additional information

Funding

This work was supported by the Northern Arizona University (NAU) Technology Research Investment Fund.