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A system for adaptive high-variability segmental perceptual training: Implementation, effectiveness, transfer
|Title:||A system for adaptive high-variability segmental perceptual training: Implementation, effectiveness, transfer|
|Keywords:||Computer-Assisted Language Learning|
Second Language Acquisition
|Issue Date:||01 Feb 2018|
|Publisher:||University of Hawaii National Foreign Language Resource Center|
Michigan State University Center for Language Education and Research
|Citation:||Qian, M., Chukharev-Hudilainen, E., & Levis, J. (2018). A system for adaptive high-variability segmental perceptual training: implementation, effectiveness, transfer. Language Learning & Technology, 22(1), 69–96. https://dx.doi.org/10125/44582|
|Abstract:||Many types of L2 phonological perception are often difficult to acquire without instruction. These difficulties with perception may also be related to intelligibility in production. Instruction on perception contrasts is more likely to be successful with the use of phonetically variable input made available through computer-assisted pronunciation training. However, few computer-assisted programs have demonstrated flexibility in diagnosing and treating individual learner problems or have made effective use of linguistic resources such as corpora for creating training materials. This study introduces a system for segmental perceptual training that uses a computational approach to perception utilizing corpus-based word frequency lists, high variability phonetic input, and text-to-speech technology to automatically create discrimination and identification perception exercises customized for individual learners. The effectiveness of the system is evaluated in an experiment with pre- and post-test design, involving 32 adult Russian-speaking learners of English as a foreign language. The participants’ perceptual gains were found to transfer to novel voices, but not to untrained words. Potential factors underlying the absence of word-level transfer are discussed. The results of the training model provide an example for replication in language teaching and research settings.|
|Appears in Collections:||Volume 22 Number 1, February 2018|
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