# User-friendly automatic transcription of low-resource languages: Plugging ESPnet into Elpis

arXiv: https://arxiv.org/abs/2101.03027 · HTML: https://mlsanity.com/paper/2101.03027/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Dec 2020.

## Built on by (most cited first, 5 of 5)

- [Breaking the Transcription Bottleneck: Fine-tuning ASR Models for Extremely Low-Resource Fieldwork Languages](https://mlsanity.com/paper/2506.17459/) (2025)
- [Spoken Term Detection Methods for Sparse Transcription in Very Low-resource Settings](https://mlsanity.com/paper/2106.06160/) (2021)
- [Learnings from Technological Interventions in a Low Resource Language: Enhancing Information Access in Gondi](https://mlsanity.com/paper/2211.16172/) (2022)
- [Supporting SENCOTEN Language Documentation Efforts with Automatic Speech Recognition](https://mlsanity.com/paper/2507.10827/) (2025)
- [Easper: An Accessible ASR Pipeline for Language Documentation](https://mlsanity.com/paper/2608.11629/) (2026)

## Abstract (arXiv)

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.