Leveraging End-to-End ASR for Endangered Language Documentation: An Empirical Study on Yolóxochitl Mixtec

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics, pages 1134–1145 April 19 - 23, 2021. ©2021 Association for Computational Linguistics 1134 Leveraging End-to-End ASR for Endangered Language Documentation: An Empirical Study on Yolox´ochitl Mixtec Jiatong Shi1 Jonathan D. Amith2 Rey Castillo Garc´ıa3 Esteban Guadalupe Sierra Kevin Duh1 Shinji Watanabe1 1The Johns Hopkins University, Baltimore, Maryland, United States 2Department of Anthropology, Gettysburg College 3Secretar´ıa de Educaci´on P´ublica, Estado de Guerrero, Mexico {jiatong shi@, kevinduh@cs.}jhu.edu {jonamith, reyyoloxochitl, estebanyoloxochitl}@gmail.com shinjiw@ieee.org Abstract “Transcription bottlenecks”, created by a shortage of effective human transcribers are one of the main challenges to endangered lan- guage (EL) documentation. Automatic speech recognition (ASR) has been suggested as a tool to overcome such bottlenecks. Following this suggestion, we investigated the effective- ness for EL documentation of end-to-end ASR, which unlike Hidden Markov Model ASR sys- tems, eschews linguistic resources but is in- stead more dependent on large-data settings. We open source a Yolox´ochitl Mixtec EL cor- pus. First, we review our method in build- ing an end-to-end ASR system in a way that would be reproducible by the ASR community. We then propose a novice transcription correc- tion task and demonstrate how ASR systems and novice transcribers can work together to improve EL documentation. We believe this combinatory methodology would mitigate the transcription bottleneck and transcriber short- age that hinders EL documentation. 1 Introduction Grenoble et al. (2011) warned that half of the world’s 7,000 languages would disappear by the end of the 21st century. Consequently, a con- cern with endangered language documentation has emerged from the convergence of interests of two major groups: (1) native speakers who wish to document their language and cultural knowledge for future generations; (2) linguists who wish to document endangered languages to explore lin- guistic structures that may soon disappear. En- dangered language (EL) documentation aims to mitigate these concerns by developing and archiv- ing corpora, lexicons, and grammars (Lehmann, 1999). There are two major challenges: (a) Transcription Bottleneck: The creation of EL resources through documentation is extremely challenging, primarily because the traditional method to preserve primary data is not simply with audio recordings but also through time-coded tran- scriptions. In a best-case scenario, texts are pre- sented in interlinear format with aligned parses and glosses along with a free translation (Anastasopou- los and Chiang, 2017). But interlinear transcrip- tions are difficult to produce in meaningful quanti- ties: (1) ELs often lack a standardized orthography (if written at all); (2) invariably, few speakers can accurately transcribe recordings. Even a highly skilled native speaker or linguist will require a min- imum of 30 to 50 hours to simply transcribe one hour of recording (Michaud et al., 2014; Zahrer et al., 2020). Additional time is needed for parse, gloss, and translation. This creates what has been called a “transcription bottleneck”, a situation in which the expert transcribers cannot keep up with the amount of recorded material for documentation. (b) Transcriber Shortage: It is generally under- stood that any viable …

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