  In recent years, automated approaches to assessing linguistic complexity in
second language (L2) writing have made significant progress in gauging learner
performance, predicting human ratings of the quality of learner productions,
and benchmarking L2 development. In contrast, there is comparatively little
work in the area of speaking, particularly with respect to fully automated
approaches to assessing L2 spontaneous speech. While the importance of a
well-performing ASR system is widely recognized, little research has been
conducted to investigate the impact of its performance on subsequent automatic
text analysis. In this paper, we focus on this issue and examine the impact of
using a state-of-the-art ASR system for subsequent automatic analysis of
linguistic complexity in spontaneously produced L2 speech. A set of 34 selected
measures were considered, falling into four categories: syntactic, lexical,
n-gram frequency, and information-theoretic measures. The agreement between the
scores for these measures obtained on the basis of ASR-generated vs. manual
transcriptions was determined through correlation analysis. A more differential
effect of ASR performance on specific types of complexity measures when
controlling for task type effects is also presented.
