Abstract
Machine learning has revolutionised speech technologies
for major world languages, but these technologies have
generally not been available for the roughly 4,000 languages with populations of fewer than 10,000 speakers. This paper describes the development of Elpis,
a pipeline which language documentation workers with
minimal computational experience can use to build their
own speech recognition models, resulting in models being
built for 16 languages from the Asia-Pacific region. Elpis
puts machine learning speech technologies within reach
of people working with languages with scarce data, in a
scalable way. This is impactful since it enables language
communities to cross the digital divide, and speeds up
language documentation. Complete automation of the
process is not feasible for languages with small quantities of data and potentially large vocabularies. Hence
our goal is not full automation, but rather to make a
practical and effective workflow that integrates machine
learning technologies.
Original language | English |
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Pages | 200-204 |
Publication status | Published - 2018 |
Event | The 6th Intl. Workshop on Spoken Language Technologies for Under-Resourced Languages - Gurugram, India Duration: 1 Jan 2018 → … |
Conference
Conference | The 6th Intl. Workshop on Spoken Language Technologies for Under-Resourced Languages |
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Period | 1/01/18 → … |