@inproceedings{maxwelll-smith-foley-2021-developing,
title = "Developing {ASR} for {I}ndonesian-{E}nglish Bilingual Language Teaching",
author = "Maxwell-Smith, Zara and
Foley, Ben",
editor = "Solorio, Thamar and
Chen, Shuguang and
Black, Alan W. and
Diab, Mona and
Sitaram, Sunayana and
Soto, Victor and
Yilmaz, Emre and
Srinivasan, Anirudh",
booktitle = "Proceedings of the Fifth Workshop on Computational Approaches to Linguistic Code-Switching",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.calcs-1.17",
doi = "10.18653/v1/2021.calcs-1.17",
pages = "131--132",
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).",
}
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%0 Conference Proceedings
%T Developing ASR for Indonesian-English Bilingual Language Teaching
%A Maxwell-Smith, Zara
%A Foley, Ben
%Y Solorio, Thamar
%Y Chen, Shuguang
%Y Black, Alan W.
%Y Diab, Mona
%Y Sitaram, Sunayana
%Y Soto, Victor
%Y Yilmaz, Emre
%Y Srinivasan, Anirudh
%S Proceedings of the Fifth Workshop on Computational Approaches to Linguistic Code-Switching
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F maxwelll-smith-foley-2021-developing
%X 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).
%R 10.18653/v1/2021.calcs-1.17
%U https://aclanthology.org/2021.calcs-1.17
%U https://doi.org/10.18653/v1/2021.calcs-1.17
%P 131-132
Markdown (Informal)
[Developing ASR for Indonesian-English Bilingual Language Teaching](https://aclanthology.org/2021.calcs-1.17) (Maxwell-Smith & Foley, CALCS 2021)
ACL