Sparse Transcription

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26 Citations (Scopus)
186 Downloads (Pure)

Abstract

The transcription bottleneck is often cited as a major obstacle for efforts to document the world’s endangered languages and supply them with language technologies. One solution is to extend methods from automatic speech recognition and machine translation, and recruit linguists to provide narrow phonetic transcriptions and sentence-aligned translations. However, I believe that these approaches are not a good fit with the available data and skills, or with long-established practices that are essentially word based. In seeking a more effective approach, I consider a century of transcription practice and a wide range of computational approaches, before proposing a computational model based on spoken term detection which I call “sparse transcription.” This represents a shift away from current assumptions that we transcribe phones, transcribe fully, and transcribe first. Instead, sparse transcription combines the older practice of word-level transcription with interpretive, iterative, and interactive processes which are amenable to wider participation and which open the way to new methods for processing oral languages.
Original languageEnglish
Pages (from-to)713-744
Number of pages32
JournalComputational Linguistics
Volume46
Issue number4
DOIs
Publication statusPublished - Dec 2020

Bibliographical note

Publisher Copyright:
© 2020 Association for Computational Linguistics

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

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