The deficiency of competent native English speaker raters and the inherent problem with intra-rater and inter-rater reliability of the oral proficiency interview (OPI) has precluded the full-fledged implementation of English performance testing, inevitably ushering in the computer- based oral proficiency interview (COPI) as its viable alternative with the help of automatic speech recognition (ASR). The plausibility and feasibility of implementing ASR-based COPI has recently been investigated with favorable results, which warrants more sophisticated research focusing on development of desirable test methods that will meet the rigorous criteria required by high-stakes language tests. In this respect, employing varied statistical methods as correlational, regression analyses, and ANOVA, the present study attempts to explore strengths and limitations of test method facets and to identify valid test methods to maximize the validity and reliability of ASR-based COPⅠ. Within the theoretical framework of communicative language components to be measured, the statistical findings reveal that some test methods prove to be more effective than others in producing COPI test results with better discriminability and reliability. The survey of students and teachers also suggest their favorable attitudes toward utilizing the COPI for in-class evaluation. Both findings strongly corroborates potential of the COPI in question as a valid performance testing tool to measure overall communicative competence. The current research is expected not only to shed light on advancement of performance testing, but also to serve the purpose of enhancing communicative English teaching.

Serious inherent problems with practicality, intra-rater and inter-rater reliability overshadow the known positive washback effects of performance assessment in language education. In particular, it has been welldocumented that inter-rater reliability poses a serious threat to overall test validity, since individual raters necessarily measure performance according to their own subjective severity criteria in language proficiency. However, language testing has witnessed a remarkable series of breakthroughs in performance assessment during the recent advent of the information era. One such breakthrough utilizes state-of-the-art automatic speech recognition (ASR) technology for oral proficiency interviews(OPI). Granting that current forms of ASR technologies may not produce results with the reliability needed to accommodate highstakes standardized test administration, they do offer aid in approaching the thorny issues of practicality and inherent human inter-rater subjectivity. Accordingly, this paper is intended to investigate the degree to which ASR-based OPI ratings match similar human-conducted OPI ratings by employing correlational analyses on the basis of degrees of rater severity. Furthermore, this paper attempts to explore a method of enhancing the robustness of ASR-based OPI ratings which capitalizes on suprasegmental information by measuring fluency based principally on the test-takers’ response time length.