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An automatic pronunciation evaluation system using non-native teacher's speech model

비원어민 교수자 음성모델을 이용한 자동발음평가 시스템

  • Received : 2016.01.27
  • Accepted : 2016.04.08
  • Published : 2016.04.30

Abstract

An appropriate evaluation on learner's pronunciation has been an important part of foreign language education. The learners should be evaluated and receive proper feedback for pronunciation improvement. Due to the cost and consistency problem of human evaluation, automatic pronunciation evaluation system has been studied. The most of the current automatic evaluation systems utilizes underlying Automatic Speech Recognition (ASR) technology. We suggest in this work to evaluate learner's pronunciation accuracy and fluency in word-level using the ASR and non-native teacher's speech model. Through the performance evaluation on our system, we confirm the overall evaluation result of pronunciation accuracy and fluency actually represents the learner's English skill level quite accurately.

외국어 학습에서 발음학습은 가장 중요한 부분 중 하나이다. 발음학습 과정은 학습자의 발음에 대해 정확한 평가와 잘못된 발음이 있을 경우 적절한 피드백을 주어 이를 개선시키는 작업을 포함한다. 숙련된 평가자의 평가는 비용에서, 비숙련 원어민들의 평가는 일관성에서 문제가 있기 때문에 이를 보완할 수 있는 자동발음평가 시스템에 대한 연구가 진행되고 있으며 자동음성인식 기술의 활용이 각광받고 있다. 본 연구에서는 자동음성인식 기술과 비원어민 교수자의 음성 모델을 기반으로 단어 수준에서 학습자의 발음 정확성과 유창성을 평가하는 시스템을 구축하였고, 이를 통해 학습자들이 자신의 발음을 정확히 평가받고 평가결과에 따라 적절한 피드백을 받을 수 있도록 하였다. 또한 시스템의 성능평가를 통해 발음 정확성과 유창성에 대한 자동평가결과가 전반적으로 학습자의 실제 영어실력을 정확히 구분한다는 것을 확인하였다.

Keywords

References

  1. Weonhee Yun. 2009. Discrepancy between Korean and Native English Raters Evaluating the English Pronunciation Spoken by Korean Learners of English. The Journal of Linguistic Science 48, 201-217.
  2. Jonghoon Lee. 2012. Error Simulation-based Pronunciation Feedback for Korean English Learners. PhD thesis, Division of Electrical and Computer Engineering Pohang University of Science and Technology.
  3. Weonhee Yun. 2012. The Objectives of English Pronunciation Evaluations and the Usability of Machine Scoring. The Journal of Linguistic Science 61, 167-184.
  4. Hyunsong Chung, Tae-yeoub Jang, Weonhee Yun, Ilsung Yun, Jaejin Sa. 2008. A Study on Automatic Measurement of Pronunciation Accuracy of English Speech Produced by Korean Learners of English. Language and Linguistic 42, 165-196
  5. Peabody, M. A. 2011. Methods for Pronunciation Assessment in Computer Aided Lanugage Learning. PhD thesis, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
  6. Moustroufas, N. and Digalakis, V. 2007. Automatic pronunciation evaluation of foreign speakers using unknown text. In Comput. Speech Language, 219-230.
  7. Sherif Mahdy Abdou, Salah Eldeen Hamid, M. R. A. S. O. A.-H. M. S. and Nazih, W. 2006. Computer aided pronunciation learning system using speech recognition techniques, in Interspeech.
  8. Chitralekha Bhat, K.L. Srinivas, P. R. 2010. Pronunciation scoring for indian english learners using a phone recognition system. In Proceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia, 135-139.
  9. Srikanth, R. and Salsman, L. B. J. 2012. Automatic Pronunciation Evaluation And Mispronunciation Detection Using CMUSphinx. In 24th International Conference on Computational Linguistics 61-68.
  10. Needleman, Saul B., and Christian D. Wunsch. 1970. A general method applicable to the search for similarities in the amino acid sequence of two proteins. Journal of molecular biology 48.3, 443-453. https://doi.org/10.1016/0022-2836(70)90057-4
  11. W. Walker, P. Lamere, P. Kwok, B. Raj, R. Singh, E. Gouvea, P. Wolf, and J. Woelfel. 2004. Sphinx-4: A flexible open source framework for speech recognition. Sun Microsystems Inc. Technical Report SML1 TR2004-0811.
  12. Hauswald, Johann, et al.. 2015. Sirius: An open end-to-end voice and vision personal assistant and its implications for future warehouse scale computers. Proceedings of the Twentieth International Conference on Architectural Support for Programming Languages and Operating Systems. ACM.
  13. Harrison, A. M., Lau, W. Y., Meng, H. M., and Wang, L. 2009. Improving mispronunciation detection and diagnosis of learners' speech with context-sensitive phonological rules based on language transfer. In INTERSPEECH 2787-2790.
  14. Witt, S. M., and Young, S. J. 1997. Language learning based on non-native speech recognition. In Eurospeech.
  15. Kim, S. D., Kim, W. S., & Woo, I. S. 2011. A Study on the Multilingual Speech Recognition using International Phonetic Language. Journal of the Korea Academia-Industrial cooperation Society, 12(7), 3267-3274. https://doi.org/10.5762/KAIS.2011.12.7.3267
  16. Jong-Young Ahn, Sang-Bum Kim, Su-Hoon Kim, Kang-In Hur, 2011. A study on Voice Recognition using Model Adaptation HMM for Mobile Environment Journal of Institute of Internet, Broadcasting and Communication (IIBC).