This paper extends the use of Generalizability Theory to the measurement of extemporaneous L2 speech through the lens of speech perception. Using six datasets of previous studies, it reports on "G studies"--a method of breaking down measurement variance--and "D studies"--a predictive study of the impact on reliability when…

Whereas many scholars have emphasized the relative importance of "comprehensibility" as an ecologically valid goal for L2 speech training, testing, and development, eliciting listeners' judgments is time-consuming. Following calls for research on more efficient L2 speech rating methods in applied linguistics, and growing attention toward…

Drawing on Glenn Fulcher's extensive work in performance-based language assessment of speaking, this paper explores the assessment of L2 speaking ability in local language testing contexts. For that purpose, I review Fulcher's influential work that highlights the relationship between the speaking construct, the task, the performance, and the…

In language performance tests, raters are important as their scoring decisions determine which aspects of performance the scores represent; however, raters are considered as one of the potential sources contributing to unwanted variability in scores (Davis, 2012). Although a great number of studies have been conducted to unpack how rater…

Through Many-Facet Rasch analysis, this study explores the rating differences between 1 computer automatic rater and 5 expert teacher raters on scoring 119 students in a computerized English listening-speaking test. Results indicate that both automatic and the teacher raters demonstrate good inter-rater reliability, though the automatic rater…

In this study, we examined whether interpreters and interpreting trainees are better predisposed to respeaking than people with no interpreting skills. We tested 57 participants (22 interpreters, 23 translators and 12 controls) while respeaking 5-minute videos with two parameters: speech rate (fast/slow) and number of speakers (one/many). Having…