The effectiveness of automatic speech recognition in ESL/EFL pronunciation: A meta-analysis

Thuy Thi Nhu Ngo, Howard Hao Jan Chen*, Kyle Kuo Wei Lai

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)

Abstract

This meta-analytic study explores the overall effectiveness of automatic speech recognition (ASR) on ESL/ EFL student pronunciation performance. Data with 15 studies representing 38 effect sizes found from 2008 to 2021 were meta-analyzed. The findings of the meta-analysis indicated that ASR has a medium overall effect size (g = 0.69). Results from moderator analyses suggest that (1) ASR with explicit corrective feedback is largely effective, while ASR with indirect feedback (e.g. ASR dictation) is moderately effective; (2) ASR has a large effect on segmental pronunciation but a small effect on suprasegmental pronunciation; (3) medium to long treatment duration of ASR results in higher learning outcomes, but short duration offers no differential effect compared to a non-ASR condition; (4) practicing pronunciation with peers in an ASR condition produces a large effect, but the effect is small when practicing alone; (5) ASR is largely effective for adult (i.e. 18 years old and above) and intermediate English learners. Overall, ASR is a beneficial application and is recommended for assisting L2 student pronunciation development.

Original languageEnglish
Pages (from-to)4-21
Number of pages18
JournalReCALL
Volume36
Issue number1
DOIs
Publication statusPublished - 2024 Jan 1

Keywords

  • ASR
  • automatic speech recognition
  • effectiveness
  • meta-analysis
  • pronunciation
  • speech technology

ASJC Scopus subject areas

  • Education
  • Language and Linguistics
  • Linguistics and Language
  • Computer Science Applications

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