Article published In:
Revista Española de Lingüística Aplicada/Spanish Journal of Applied Linguistics: Online-First Articles

The Spanish Computer-Assisted Listening and Speaking Tutor

A multilingual approach to pronunciation training

ORCID logo | Universitat de València
ORCID logo | Norwegian University of Science and Technology

This article presents the Spanish version of the Computer-Assisted Listening and Speaking Tutor (CALST), an online platform that can be used to complement pronunciation training in the classroom. The Spanish CALST offers listening, speaking, and spelling exercises for Northern-Central Peninsular Spanish as an L2. Exercises are tailored by an automatic comparison with the learner’s native language based on a database of phonetically specified phoneme inventories for over 500 languages, with the result that learners with different L1s are exposed to different exercises adapted to their specific needs. In this article, we present a description of the exercises as well as the criteria used to develop Spanish content for CALST. We discuss the limitations of the platform, the logging of user results as a partial solution to these limitations, and the possible future use of the logged data to increase our understanding of L2 acquisition.

Keywords: pronunciation training, Spanish L2, computer-assisted, discrimination, identification, orthography, multilingual
Article outline
  • 1.Introduction
    • 1.1Challenges for Spanish pronunciation training
    • 1.2The significance of the learner’s native language
  • 2.A multilingual approach to online pronunciation training
    • 2.1L1‑L2map and its use in CALST
    • 2.2CALST
    • 2.3Beyond single segments
  • 3.Developing the Spanish CALST
    • 3.1Informants
    • 3.2Spanish phonological inventory in L1‑L2map
    • 3.3Phonological comparison, allophonic training
    • 3.4Criteria for exercise creation
      • 3.4.1Exercises contrasting individual phonemes and their allophones
      • 3.4.2Consonant cluster and stress exercises
    • 3.5Selecting content for the exercises
  • 4.Limitations and future exploitation
  • 5.Conclusions
  • Acknowledgements
  • References
Available under the Creative Commons Attribution (CC BY) 4.0 license.

For any use beyond this license, please contact the publisher at [email protected].

Published online: 5 April 2024
DOI logo
https://doi.org/10.1075/resla.22046.mar
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