Sparse Transcription

Author:

Bird Steven1

Affiliation:

1. Northern Institute, Charles Darwin University.

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 that 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 that are amenable to wider participation and that open the way to new methods for processing oral languages.

Publisher

MIT Press - Journals

Subject

Artificial Intelligence,Computer Science Applications,Linguistics and Language,Language and Linguistics

Reference131 articles.

1. Adams, Oliver. 2017. Automatic Understanding of Unwritten Languages. Ph.D. thesis, University of Melbourne.

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1. Keyword Localisation in Untranscribed Speech Using Visually Grounded Speech Models;IEEE Journal of Selected Topics in Signal Processing;2022-10

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3. Leveraging Pre-Trained Representations to Improve Access to Untranscribed Speech from Endangered Languages;2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU);2021-12-13

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