Speech recognition for newly documented languages: highly encouraging tests using automatically generated phonemic transcription of Yongning Na audio recordings

Automatic speech recognition tools have strong potential for facilitating language documentation. This blog note reports on highly encouraging tests using automatic transcription in the documentation of Yongning Na, a Sino-Tibetan language of Southwest China. After twenty months of fieldwork (spread over twelve years, from 2006 to 2017), 14 hours of speech had been recorded, of which 5.5 hours were transcribed (200 minutes of narratives and 130 minutes of morphotonology elicitation sessions). Oliver Adams, the author of mam, an open-source software tool for developing multilingual acoustic models, volunteered to experiment with these data. He trained a single-speaker automatic speech transcription tool over the transcribed materials and applied it to untranscribed audio files. The error rate is low: on the order of 17% of errors in phoneme identification. This makes the automatic transcriptions useful as a canvas for the linguist, who corrects mistakes and produces the translation in collaboration with language consultants.

Today I am posting my initial report to Oliver Adams, written in the field in Yunnan, on May 12th, 2017. I consider this date as a landmark in my work documenting Yongning Na! This document remained confidential until today because its online availability would have de-anonymized a submission by Oliver Adams & collaborators that was under double-blind review for a workshop in Australia (the Australasian Language Technology Association Workshop, Dec. 6-8, 2017). This paper, which presents Oliver’s work to an audience of language technology specialists, is now available here.
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Alexis Michaud

I’m a linguist specialized in phonetics, focusing on un(der-)documented languages.

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