Ben Foley


2021

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Developing ASR for Indonesian-English Bilingual Language Teaching
Zara Maxwelll-Smith | Ben Foley
Proceedings of the Fifth Workshop on Computational Approaches to Linguistic Code-Switching

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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User-friendly Automatic Transcription of Low-resource Languages: Plugging ESPnet into Elpis
Oliver Adams | Benjamin Galliot | Guillaume Wisniewski | Nicholas Lambourne | Ben Foley | Rahasya Sanders-Dwyer | Janet Wiles | Alexis Michaud | Séverine Guillaume | Laurent Besacier | Christopher Cox | Katya Aplonova | Guillaume Jacques | Nathan Hill
Proceedings of the 4th Workshop on the Use of Computational Methods in the Study of Endangered Languages Volume 1 (Papers)

2020

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Applications of Natural Language Processing in Bilingual Language Teaching: An Indonesian-English Case Study
Zara Maxwelll-Smith | Simón González Ochoa | Ben Foley | Hanna Suominen
Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications

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. In this paper, we set out methodological considerations of using automated speech recognition to build a corpus of teacher speech in an Indonesian language classroom. Our preliminary results (64% word error rate) suggest these tools have the potential to speed data collection in this context. We provide practical examples of our data structure, details of our piloted computer-assisted processes, and fine-grained error analysis. Our study is informed and directed by genuine research questions and discussion in both the education and computational linguistics fields. We highlight some of the benefits and risks of using these emerging technologies to analyze the complex work of language teachers and in education more generally.

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Scaling Language Data Import/Export with a Data Transformer Interface
Nicholas Buckeridge | Ben Foley
Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL)

This paper focuses on the technical improvement of Elpis, a language technology which assists people in the process of transcription, particularly for low-resource language documentation situations. To provide better support for the diversity of file formats encountered by people working to document the world’s languages, a Data Transformer interface has been developed to abstract the complexities of designing individual data import scripts. This work took place as part of a larger project of code quality improvement and the publication of template code that can be used for development of other language technologies.

2019

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Future Directions in Technological Support for Language Documentation
Daan van Esch | Ben Foley | Nay San
Proceedings of the 3rd Workshop on the Use of Computational Methods in the Study of Endangered Languages Volume 1 (Papers)