User-friendly automatic transcription of low-resource languages: Plugging ESPnet into Elpis
Oliver Adams
Benjamin Galliot
Guillaume Wisniewski
Nicholas Lambourne
Ben Foley
Rahasya Sanders-Dwyer
Janet Wiles
Alexis Michaud
Severine Guillaume
Laurent Besacier
Christopher Cox
Katya Aplonova
Guillaume Jacques
Nathan W. Hill

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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