Identifying fluency parameters for a machine-learning-based automated interpreting assessment system
نویسندگان
چکیده
Fluency is an important yet difficult-to-measure criterion in interpreting assessment. This empirical study of English-Chinese consecutive aims to identify fluency parameters for a machine-learning-based automated assessment system. The main findings include: (a) evidence supports the choice median values as cut-offs unfilled pauses and articulation rate; (b) it informs selection outliers particularly long pauses, relatively slow articulation; (c) number filled articulation, mean length can be chosen build machine-learning models predict future studies they explain variance established temporal measures show stronger explanatory power than dependent variables when predicting scores. identifies rubrics on basis provides methodological solution automate labour-intensive tasks assessments.
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ژورنال
عنوان ژورنال: Perspectives
سال: 2022
ISSN: ['1360-3108', '1460-7018']
DOI: https://doi.org/10.1080/0907676x.2022.2133618