Leveraging End-to-End ASR for Endangered Language Documentation: An Empirical Study on Yolox\'ochitl Mixtec

Autor:	Shi, Jiatong, Amith, Jonathan D., García, Rey Castillo, Sierra, Esteban Guadalupe, Duh, Kevin, Watanabe, Shinji
Rok vydání:	2021
Předmět:	Electrical Engineering and Systems Science - Audio and Speech Processing Computer Science - Sound
Druh dokumentu:	Working Paper
Popis:	"Transcription bottlenecks", created by a shortage of effective human transcribers 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 Yolox\'ochitl 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. Comment: Accepted by EACL2021
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2101.10877 Zobrazit plný text záznamu View this record from Arxiv