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.

In this paper we discuss the methodology behind the construction of elicited imitation (EI) test items. First we examine varying uses for EI tests in research and in testing overall oral proficiency. We also mention criticisms of previous test items. Then we identify the factors that contribute to the difficulty of an EI item as shown in previous studies. Based on this discussion, we describe a way of automating the creation of test items in order to better evaluate language learners' oral proficiency while improving item naturalness. We present a new item construction tool and the process that it implements in order to create test items from a corpus, identifying relevant features needed to compile a database of EI test items. We examine results from administration of a new EI test engineered in this manner, illustrating the effect that standard language resources can have on creating an effective EI test item repository. We also sketch ongoing work on test item generation for other languages and an adaptive test that will use this collection of test items.