A system for adaptive high-variability segmental perceptual training: Implementation, effectiveness, transfer

Date
2018-02-01
Authors
Levis, John
Qian, Manman
Chukharev-Hudilainen, Evgeny
Chukharev-Hudilainen, Evgeny
Levis, John
Major Professor
Advisor
Committee Member
Journal Title
Journal ISSN
Volume Title
Publisher
Altmetrics
Research Projects
Organizational Units
English
Organizational Unit
Journal Issue
Series
Department
English
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.

Description
Keywords
Citation
DOI
Collections