Campus Units

English

Document Type

Article

Publication Version

Published Version

Publication Date

2-2018

Journal or Book Title

Language Learning & Technology

Volume

22

Issue

1

First Page

69

Last Page

96

DOI

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 corpusbased 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.

Comments

This article is published as 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. DOI: 10125/44582. Posted with permission.

Copyright Owner

The Author(s)

Language

en

File Format

application/pdf

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