Corpus-based discourse analysis: Automatic Speech Recognition (ASR) technologies and spoken corpus collection

Nargiza Asrorova

doi:10.47689/2181-3701-vol3-iss4-pp1-8

Nargiza Asrorova PhD student Uzbekistan State World Languages University, Teacher, TIFT University

DOI

https://doi.org/10.47689/2181-3701-vol3-iss4-pp1-8

Keywords

Automatic Speech Recognition (ASR) , speech transcription , corpus , spoken discourse analysis , second language acquisition

Abstract

This study investigates the integration of Automatic Speech Recognition (ASR) technologies in the collection and analysis of spoken learner corpora, with a focus on L2 contexts. Employing a mixed-methods design, the research evaluates the effectiveness of ASR systems, specifically, Whisper and BERT-based models in producing accurate transcriptions and facilitating language acquisition. Quantitative results demonstrate high transcription accuracy, while qualitative data reveal that ASR-supported feedback significantly enhances learner engagement, pronunciation, and speaking proficiency. Technological limitations and ethical concerns related to data privacy and feedback mechanisms are also taken into account. Overall, the findings highlight the transformative potential of ASR technologies in language education by enabling scalable, real-time assessment and personalized feedback, while underscoring the need for continued refinement and equitable implementation across diverse learner populations.

References

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Downloads

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Published

2025-07-15

How to Cite

Asrorova, N. 2025. Corpus-based discourse analysis: Automatic Speech Recognition (ASR) technologies and spoken corpus collection. Foreign Linguistics and Lingvodidactics. 3, 4 (Jul. 2025), 1–8. DOI:https://doi.org/10.47689/2181-3701-vol3-iss4-pp1-8.