Quantifying the impact of ASR-based instruction: What does the iSpraak platform learner data show?
Year: 2024
Type: article
Abstract: Computer-assisted Pronunciation Training (CAPT) tools have become increasingly dependent on Automatic Speech Recognition (ASR) technology to provide automated corrective pronunciation feedback to learners. The extent to which ASR-based tools measurably improve second language (L2) pronunciation is of great interest to language educators globally, and Computer-assisted Language Learning (CALL) researchers. Studies to date have largely been conducted by research practitioners with small-to-medium sized samples at single institutions. The findings and conclusions drawn from such small-scale data collection might be significantly bolstered by analysing the vast stores of learner data from large CAPT platforms. This study is informed by a sizable eight-year dataset from iSpraak, an open-source pronunciation tool designed to model and evaluate L2 speech. Quantitative analysis of anonymised learner interactions with this application reveals significant gains in intelligibility measures across multiple languages. Results also suggest that the extent of ASR’s ability to improve learner pronunciation may be L2 dependent.
Source: The EuroCALL Review
Author: Dan Nickolai
Institution: Saint Louis University
Language: English
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
SDG: Quality education
Open Access status: diamond
OpenAlex ID: W4403478593