Cognitive representation of phonological categories: The evidence from Mandarin speakers' learning of Cantonese tones

Kaile Zhang, Yonghong Li, Gang Peng

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Even when acoustic tokens vary substantially, they nevertheless can usually be recognized accurately. Two opposing models have been proposed to account for how the speech recognition mechanism works to achieve the perceptual consistency. The abstract model holds that there is a unitary cognitive representation for each phonological category. The speech signal, after having variations filtered out by a computational process, is matched to a particular representation. By contrast, the exemplar-based model holds that the previously encountered exemplars of a given speech category together form its mental representation. The speech recognition for this model involves searching for a match (based on similarity) between the incoming signal and stored exemplars. The present study tested which of these two models best fit data from second language acquisition. Mandarin speakers were trained with Cantonese tones that differed in acoustic variability. Results showed that training materials involving a large degree of within-class variability didn't produce a better learning outcome than those involving a small degree of variability, suggesting that the abstract model may provide a better fit for this data. The characteristics of Mandarin speakers' acquisition of Cantonese tones were also discussed.
Original languageEnglish
Title of host publicationProceedings of 2016 10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016
PublisherIEEE
ISBN (Electronic)9781509042937
DOIs
Publication statusPublished - 2 May 2017
Event10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016 - Tianjin, China
Duration: 17 Oct 201620 Oct 2016

Conference

Conference10th International Symposium on Chinese Spoken Language Processing, ISCSLP 2016
CountryChina
CityTianjin
Period17/10/1620/10/16

Keywords

  • Second language learning
  • Speech recognition
  • The abstract model
  • The exemplar-based model

ASJC Scopus subject areas

  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Linguistics and Language

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