User-friendly automatic transcription of low-resource languages: Plugging ESPnet into Elpis
December 15, 2020 ยท Declared Dead ยท ๐ COMPUTEL
"No code URL or promise found in abstract"
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Authors
Oliver Adams, Benjamin Galliot, Guillaume Wisniewski, Nicholas Lambourne, Ben Foley, Rahasya Sanders-Dwyer, Janet Wiles, Alexis Michaud, Sรฉverine Guillaume, Laurent Besacier, Christopher Cox, Katya Aplonova, Guillaume Jacques, Nathan Hill
arXiv ID
2101.03027
Category
cs.CL: Computation & Language
Cross-listed
cs.AI,
eess.SP
Citations
13
Venue
COMPUTEL
Last Checked
5 months ago
Abstract
This paper reports on progress integrating the speech recognition toolkit ESPnet into Elpis, a web front-end originally designed to provide access to the Kaldi automatic speech recognition toolkit. The goal of this work is to make end-to-end speech recognition models available to language workers via a user-friendly graphical interface. Encouraging results are reported on (i) development of an ESPnet recipe for use in Elpis, with preliminary results on data sets previously used for training acoustic models with the Persephone toolkit along with a new data set that had not previously been used in speech recognition, and (ii) incorporating ESPnet into Elpis along with UI enhancements and a CUDA-supported Dockerfile.
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