Evaluating Interpreter's Skill by Measurement of Prosody Recognition
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Sign language is a visual language in which main articulators are hands, torso, head, and face. For simultaneous interpreters of Japanese sign language (JSL) and spoken Japanese, it is very important to recognize not only the hands movement but also prosody such like head, eye, posture and facial expression. This is because prosody has grammatical rules for representing the case and modification relations in JSL. The goal of this study is to introduce an examination called MPR (Measurement of Prosody Recognition) and to demonstrate that it can be an indicator for the other general skills of interpreters. For this purpose, we conducted two experiments: the first studies the relationship between the interpreter's experience and the performance score on MPR (Experiment-1), and the second investigates the specific skill that can be estimated by MPR (Experiment-2). The data in Experiment-1 came from four interpreters who had more than 1-year experience as interpreters, and more four interpreters who had less than 1-year experience. The mean accuracy of MPR in the more experienced group was higher than that in the less experienced group. The data in Experiment-2 came from three high MPR interpreters and three low MPR interpreters. Two hearing subjects and three deaf subjects evaluated their skill in terms of the speech or sign interpretation skill, the reliability of interpretation, the expeditiousness, and the subjective sense of accomplishment for the ordering pizza task. The two experiments indicated a possibility that MPR could be useful for estimating if the interpreter is sufficiently experienced to interpret from sign language to spoken Japanese, and if they can work on the interpretation expeditiously without making the deaf or the hearing clients anxious. Finally we end this paper with suggestions for conclusions and future work.
- Transactions of the Japanese Society for Artificial Intelligence
Transactions of the Japanese Society for Artificial Intelligence 23(3), 117-126, 2008
The Japanese Society for Artificial Intelligence