User-friendly automatic transcription of low-resource languages: Plugging ESPnet into Elpis
This work aims to provide language workers with an easier way to access and utilize end-to-end speech recognition models for low-resource languages, representing an incremental improvement in accessibility.
This paper integrates the ESPnet speech recognition toolkit into the Elpis web front-end, making end-to-end speech recognition models accessible to language workers through a user-friendly graphical interface. They developed an ESPnet recipe for Elpis and incorporated it, reporting encouraging preliminary results on existing and new datasets.
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.