Developing ASR for Indonesian-English Bilingual Language Teaching

Zara Maxwelll-Smith, Ben Foley


Abstract
Usage-based analyses of teacher corpora and code-switching (Boztepe, 2003) are an important next stage in understanding language acquisition. Multilingual corpora are difficult to compile and a classroom setting adds pedagogy to the mix of factors which make this data so rich and problematic to classify. Using quantitative methods to understand language learning and teaching is difficult work as the ‘transcription bottleneck’ constrains the size of datasets. We found that using an automatic speech recognition (ASR) toolkit with a small set of training data is likely to speed data collection in this context (Maxwelll-Smith et al., 2020).
Anthology ID:
2021.calcs-1.17
Volume:
Proceedings of the Fifth Workshop on Computational Approaches to Linguistic Code-Switching
Month:
June
Year:
2021
Address:
Online
Venues:
CALCS | NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
131–132
Language:
URL:
https://aclanthology.org/2021.calcs-1.17
DOI:
10.18653/v1/2021.calcs-1.17
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.calcs-1.17.pdf