Shallow analysis based assessment of syntactic complexity for automated speech scoring

Abstract

Designing measures that capture various aspects of language ability is a central task in the design of systems for automatic scoring of spontaneous speech. In this study, we address a key aspect of language proficiency assessment - syntactic complexity. We propose a novel measure of syntactic complexity for spontaneous speech that shows optimum empirical performance on real world data in multiple ways. First, it is both robust and reliable, producing automatic scores that agree well with human rating compared to the stateof- the-art. Second, the measure makes sense theoretically, both from algorithmic and native language acquisition points of view. © 2014 Association for Computational Linguistics.