User-friendly automatic transcription of low-resource languages: Plugging ESPnet into Elpis

Adams, Oliver; Galliot, Benjamin; Wisniewski, Guillaume; Lambourne, Nicholas; Foley, Ben; Sanders-Dwyer, Rahasya; Wiles, Janet; Michaud, Alexis; Guillaume, Séverine; Besacier, Laurent; Cox, Christopher; Aplonova, Katya; Jacques, Guillaume; Hill, Nathan W.

doi:10.48550/arxiv.2101.03027

Cited by 1 publication

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“…Phonemic transcription is also now possible in ELPIS using ESPnet (Watanabe et al 2018), a neural network speech-processing toolkit that allows ELPIS to be used without a pronunciation lexicon or other language-specific preprocessing. Less preprocessing and an emphasis on user-friendly interfaces are intended to make ELPIS more easily accessible to language workers without computational training (Adams et al 2021).…”

Section: Nlp For Annotationmentioning

confidence: 99%

Recent Advances in Technologies for Resource Creation and Mobilization in Language Documentation

Berez-Kroeker

Gabber

Slayton

2023

Annu. Rev. Linguist.

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Language documentation as a subfield of linguistics has arisen over the past roughly two and a half decades more or less simultaneously with the widespread availability of inexpensive hardware and software for creating, storing, and sharing digital objects. Thus, in some ways the history of advancements within the discipline is also a history of how technological tools have been developed, tested, adopted, and eventually abandoned as newer technologies appear. In this article we examine some recent technologies used both for creating documentary resources, usually considered to include recorded language events in a variety of genres and settings and enough annotation to make them decipherable, and for then mobilizing those resources so that they can be used and shared in language learning, reclamation, revitalization, and analysis. Expected final online publication date for the Annual Review of Linguistics, Volume 9 is January 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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Section: Nlp For Annotationmentioning

confidence: 99%