Evaluation of Student Interpreters Using Voice Recognition and Automatic Grammar Correction

Abstract

The evaluation and assessment of student interpreters have long been an issue for interpreting programs. The balance between student practice throughput, the time and human cost of assessment, and the quality of feedback is notoriously difficult to achieve. Here we demonstrate a way to rapidly assess student Chinese-to-English interpreting performance using automatic speech recognition and grammar correction software. The assessment results are compared with human graders against a set of criteria for grammar, fidelity, register, and enunciation. The results show that the semiautomatic assessment process is less time-consuming, and can give adequate feedback for enunciation, grammar, and register. Student volunteers were able to maintain engagement over a three-month period with minimal intervention from the instructor, however, interest began to drop over the long term.