영어발음 평가 목표와 자동발음평가 활용의 타당성
The Objectives of English Pronunciation Evaluations and the Usability of Machine Scoring.
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This study examined the objectives of English pronunciation evaluations in the EFL setting and discussed the effectiveness of the automatic pronunciation scoring systems employing techniques to measure the similarity between non-native and native speech. Since the current English education is directed toward communicative language teaching, the goal of fluency is regarded as being more desirable than that of accuracy, resulting in less attention to the importance of native-like pronunciations. Alternative goals suggested from the hearer’s point of view are the intelligibility and the comprehensibility. However, human raters’ judgement can be varied depending on the raters’ native language background and their proficiency of the target language. Machine scoring of the pronunciation could supplement the inconsistency that human scoring brings about. The HMM-based systems still look for the native-like speech as most desirable without consideration of intelligible and comprehensible speech. Many system developers use the human scoring for system calibration but the criteria for human scoring is still mainly on the accentedness. With the idea of intelligible and comprehensible speech, more careful approaches to the machine scoring should be taken.
Abstract
This study examined the objectives of English pronunciation evaluations in the EFL setting and discussed the effectiveness of the automatic pronunciation scoring systems employing techniques to measure the similarity between non-native and native speech. Since the current English education is directed toward communicative language teaching, the goal of fluency is regarded as being more desirable than that of accuracy, resulting in less attention to the importance of native-like pronunciations. Alternative goals suggested from the hearer’s point of view are the intelligibility and the comprehensibility. However, human raters’ judgement can be varied depending on the raters’ native language background and their proficiency of the target language. Machine scoring of the pronunciation could supplement the inconsistency that human scoring brings about. The HMM-based systems still look for the native-like speech as most desirable without consideration of intelligible and comprehensible speech. Many system developers use the human scoring for system calibration but the criteria for human scoring is still mainly on the accentedness. With the idea of intelligible and comprehensible speech, more careful approaches to the machine scoring should be taken.
참고문헌 (31)
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