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Predicting Potential Difficulties in Second Language Lexical Tone Learning with Support Vector Machine Models

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Learning Technologies and Systems (SETE 2020, ICWL 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12511))

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Abstract

Second language speech learning is affected by learners’ native language backgrounds. Teachers can facilitate learning by tailoring their pedagogy to cater for unique difficulties induced by native language interference. The present study employed Support Vector Machine (SVM) models to simulate how naïve listeners of diverse tone languages will assimilate non-native lexical tone categories into their native categories. Based on these simulated assimilation patterns and extrapolating basic principles from the Perceptual Assimilation Model (Best 1995), we predicted potential learning difficulties for each group. The results offer teachers guidance concerning which tone(s) to emphasize when instructing students from particular language backgrounds.

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Notes

  1. 1.

    One token was mispronounced and thus was deleted.

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Acknowledgements

This research was supported by a China Scholarship Council and Western Sydney University Joint scholarship awarded to the first author, Juqiang Chen.

Author information

Authors and Affiliations

  1. The MARCS Institute for Brain Behaviour and Development, Western Sydney University, Penrith, NSW, 2751, Australia

    Juqiang Chen, Catherine T. Best & Mark Antoniou

  2. Haskins Laboratories, New Haven, CT, USA

    Catherine T. Best

Authors
  1. Juqiang Chen
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  2. Catherine T. Best
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  3. Mark Antoniou
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Corresponding author

Correspondence to Juqiang Chen .

Editor information

Editors and Affiliations

  1. Ningbo Institute of Industrial Technology, Ningbo, China

    Chaoyi Pang

  2. College of Computer Science, Zhejiang University, Hangzhou, China

    Yunjun Gao

  3. Monash University, Clayton, VIC, Australia

    Guanliang Chen

  4. Faculty of Automation, Computers and Electronics, University of Craiova, Craiova, Romania

    Elvira Popescu

  5. Department of Physics, Zhejiang University, Hangzhou, China

    Lu Chen

  6. School of Computer Science, South China Normal University, Guangzhou, China

    Tianyong Hao

  7. Zhejiang University, Ningbo, China

    Bailing Zhang

  8. Universidad Internacional De La Rioja, Logroño, Spain

    Silvia Margarita Baldiris Navarro

  9. Department of Computing, Hong Kong Polytechnic University, Kowloon, Hong Kong

    Qing Li

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Chen, J., Best, C.T., Antoniou, M. (2021). Predicting Potential Difficulties in Second Language Lexical Tone Learning with Support Vector Machine Models. In: Pang, C., et al. Learning Technologies and Systems. SETE ICWL 2020 2020. Lecture Notes in Computer Science(), vol 12511. Springer, Cham. https://doi.org/10.1007/978-3-030-66906-5_36

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  • DOI: https://doi.org/10.1007/978-3-030-66906-5_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-66905-8

  • Online ISBN: 978-3-030-66906-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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