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The Prosodic Changes of Korean English Learners in Robot Assisted Learning

로봇보조언어교육을 통한 초등 영어 학습자의 운율 변화

  • In, Jiyoung (Dept. of Linguistics, Chungnam National University) ;
  • Han, JeongHye (Cheongju National University of Education)
  • 인지영 (충남대학교 언어학과) ;
  • 한정혜 (청주교육대학교 컴퓨터교육학과)
  • Received : 2016.06.08
  • Accepted : 2016.08.25
  • Published : 2016.08.31

Abstract

A robot's recognition and diagnosis of pronunciation and its speech are the most important interactions in RALL(Robot Assisted Language Learning). This study is to verify the effectiveness of robot TTS(Text to Sound) technology in assisting Korean English language learners to acquire a native-like accent by correcting the prosodic errors they commonly make. The child English language learners' F0 range and speaking rate in the 4th grade, a prosodic variable, will be measured and analyzed for any changes in accent. We compare whether robot with the currently available TTS technology appeared to be effective for the 4th graders and 1st graders who were not under the formal English learning with native speaker from the acoustic phonetic viewpoint. Two groups by repeating TTS of RALL responded to the speaking rate rather than F0 range.

로봇의 발음인식과 진단 그리고 발음빠르기는 로봇보조언어교육의 가장 중요한 상호작용이다. 이 연구는 한국인 초등 영어 학습자를 위하여 음율적 오류를 수정함으로써 원어민과 같은 억양을 산출하기 위한 로봇음성합성기의 효과성을 측정하기 위한 것이다. 이를 위해 초등 4학년 영어학습자들의 F0 범위값과 발화 속도라는 음성음향적 변수를 측정하여 분석하였고, 그 결과를 정규 영어교육의 시작하지 않은 1학년 학습자와 비교하였다. 로봇음성합성기를 활용한 언어학습에서 두 집단은 F0값보다 발화속도 변인에 반응하였다.

Keywords

References

  1. Authors (2015). The Prosodic Conditions in Robot's TTS for Children as Beginners in English Learning, Indian Journal of Science and Technology 8, (S5), 48-51.
  2. Authors (2015). The Acoustic-Phonetics Change of English Learners in Robot Assisted Learning, ,HRI'15 ACM/IEEE International Conference on Human-Robot Interaction extended Abstracts, March 2-5, 39-40.
  3. B.F.Pennington (1991). Diagnosing Learning Disorders: A Neuro-psychological Framework. New York: Guilford Press.
  4. George M. Chinnery (2006). Emerging Technologies-Going to the MALL: Mobile Assisted Language Learning. Language Learning & Technology, 10(1), 9-16.
  5. H. S. Chung (2009). A Study on the Rhythm of Korean EFL Learner's English Pronunciation. Phonetics and Speech Science 1(2), 141-149.
  6. J. H. Han (2010). Robot-Aided Learning and r-Learning Services. Human-Robot Interaction, ISBN: 978-953-307-051-3.
  7. J. H. Han (2012). Robot Assisted Language Learning. Language Learning & Technology, 16(3), 1-9.
  8. J. S. Yang (2013). Mobile Assisted Language Learning: Review of the Recent Application of Emerging Mobile Technology, English Language Teaching, 6(7), 19-25.
  9. M. J. Munro, & T. M Derwing (2000). Foreign accent, comprehensibility, and intelligibility in the speech of second language learners, Language Learning, 45(1), 73-97. https://doi.org/10.1111/j.1467-1770.1995.tb00963.x
  10. M. J. Munro, & T. M. Derwing (2001). Modeling conceptions of the Accentedness and comprehensibility of L2 speech-The role of speaking rate. Studies on Second Language Acquisition, 23, 451-468.
  11. Robert Godwin-Jones (2011). Emerging Technologies-MOBILE APPS FOR LANGUAGE LEARNING. Language Learning & Technology, 15(2), 2-11.
  12. R. Wong (1985). Teaching Pronunciation: Focus on English Rhythm Intonation. New York: Prentice-Hall.
  13. S. G. Guion, J. E. Flege, S. H. Liu, and G. H Yeni-Komshian (2000). Age of learning effects on the duration of sentence produced in a second language. Applied Psycholinguistics, 21, 205-228. https://doi.org/10.1017/S0142716400002034
  14. S. H. Kang, S. J. Rhee (2011). A study on the Suprasegmental Parameters Exerting an Effect on the Judgement of Goodness or Badness on Korean-spoken English. Phonetics and Speech Science, 10(4), 3-10.
  15. S. J. Lee, H. J. Noh, J. H. Lee, K. S. Lee, G. G. Lee, S. D. Sagong and M. S. Kim (2011). On the effectiveness of Robot-Assisted Language Learning. ReCALL, 23, 25-58. https://doi.org/10.1017/S0958344010000273
  16. S. J. Park, J. H. Han, and B. H. Kang (2011) Teaching assistant robot, ROBOSEM, in English class and practical issues for its diffusion. Proceedings of the IEEE ARSO (Advanced Robotics and its SOcial impacts) conference, Oct. 2-4, Half-moon bay, California, USA.
  17. S. J. Rhee, C, H. Cho, and S. Y. Moon (2003), Korean & Native Speakers' High-low Range Differences in F0 and its Role in Pronunciation Assessment. Phonetics and Speech Science, 10(4), 93-103.
  18. T. Kanda, R. Sato, N. Saiwaki, and H. Ishiguro (2007). A Two-month Field Trial in an Elementary School for Long-term HRI. IEEE Transactions on Robotics(Special Issue on Human-Robot Interaction), 23(5), 962-971. https://doi.org/10.1109/TRO.2007.904904

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