Leveraging End-to-End ASR for Endangered Language Documentation: An Empirical Study on Yolóxochitl M ixtec (2021)

Abstract: “Transcription bottlenecks”, created by a shortage of effective human transcribers (i.e., transcriber shortage), are one of the main challenges to endangered language (EL) documentation. Automatic speech recognition (ASR) has been suggested as a tool to overcome such bottlenecks. Following this suggestion, we investigated the effectiveness for EL documentation of end-to-end ASR, which unlike Hidden Markov Model ASR systems, eschews linguistic resources but is instead more dependent on large-data settings. We open source a Yoloxóchitl Mixtec EL corpus. First, we review our method in building an end-to-end ASR system in a way that would be reproducible by the ASR community. We then propose a novice transcription correction 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 shortage that hinders EL documentation.

## corpus description in this section, we first introduce the linguistic specifics for ym and ymc. then we discuss the recording settings. since ym is a spoken language without a standardized textual format, we next explain the transcription style designed for this language. finally, we offer the corpus partition and some statistics regarding corpora size. ## conclusion and future work this work presents an open-source endangered language corpus in yoloxóchitl mixtec and a comparative and reproducible study on various approaches to end-to-end asr. we demonstrate that end-toend approaches are feasible and present comparable results over conventional hmm approaches, which require resources such as language lexicons not necessary with end-to-end asr. additionally, we propose novice transcription correction as a potential task for asr in el documentation. we examine two methods to approach this task. the first is a rule-based approach that uses hierarchical dynamic alignment and linguistic rules to perform novice-asr hybridization. the second is a votingbased method that combines hypotheses from the novice and end-to-end asr systems. empirical studies on the ymc-nt corpus indicate that both methods significantly reduce the cer/wer of the novice transcription for clean speech. the above discussion suggests that a useful approach to el documentation using both human and computational (asr) resources might focus on training each system (human and asr) for particular transcription tasks. if we know from the start that asr will be used to correct novice transcriptions in areas of difficulty, we could train an asr system to maximize accuracy in those areas that challenge novice learning.