Back

Dialect‐Specific Modes Influence Second Language Production: Evidence From Bidialectal Shanghai–Mandarin Chinese Learners of English Within the Second Language Linguistic Perception Model.

  • Published In: Language Learning, 2025, v. 75, n. 4. P. 1122 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Liu, Xiaoluan; Bai, Lan; Escudero, Paola 3 of 3

Abstract

The present study investigates the impact of bidialectalism on L2 production, focusing on the role of dialect modes. Shanghai–Mandarin Chinese bidialectal speakers were recruited to produce second language (L2) English vowels under the influence of either Shanghai or Mandarin Chinese mode. Results showed that in the Shanghai mode, participants' English vowel production closely resembled that of native English speakers. Notably, Shanghai Chinese significantly influenced English vowel production in the Shanghai mode, and Mandarin Chinese had a strong impact on English vowel production in the Mandarin mode. This highlights that each first language (L1) dialect—that is, the activated dialect mode—significantly influences L2 English vowel production. The present study reveals that bidialectal speakers have differential L2 production performance depending on the L1 dialect mode that they activate. These results are interpreted within the framework of the second language linguistic perception (L2LP) model, contributing to the theoretical understanding of how L1 dialect modes influence L2 acquisition. A one‐page Accessible Summary of this article in nontechnical language is freely available in the Supporting Information online and at https://oasis‐database.org. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Language Learning. 2025/12, Vol. 75, Issue 4, p1122
  • Document Type:Article
  • Subject Area:Language and Linguistics
  • Publication Date:2025
  • ISSN:0023-8333
  • DOI:10.1111/lang.12699
  • Accession Number:189332335
  • Copyright Statement:Copyright of Language Learning is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.