Easper: An Accessible ASR Pipeline for Language Documentation

2026-08-12

AI Summary

This study addresses the challenge faced by field linguists who, due to limited technical expertise, struggle to leverage multilingual automatic speech recognition (ASR) systems, making audio transcription a bottleneck in language documentation. To overcome this, the work proposes the first no-code ASR fine-tuning workflow tailored for linguists, seamlessly integrating the ELAN annotation environment with the multilingual Whisper model via a cloud-based platform to enable iterative refinement. A novel cold-start transcription prioritization strategy is introduced, which selects utterances based on linguistic richness—such as lexical diversity and phonemic coverage—rather than acoustic quality. Experiments on three low-resource languages from Vanuatu demonstrate that this approach substantially improves both transcription efficiency and accuracy, rapidly reducing character error rate (CER) even under noisy recording conditions.

Abstract

Audio transcription is a critical bottleneck in language documentation. While multilingual Automatic Speech Recognition (ASR) models like Whisper offer solutions, field linguists often lack the expertise to utilise them. We present Easper, an open-source, no-code workflow enabling linguists to iteratively fine-tune ASR models via cloud resources directly from ELAN annotations. Deploying ASR also raises a cold start problem: deciding which recordings to transcribe first to bootstrap an accurate model. Using Easper, we evaluate transcription prioritisation strategies on three Vanuatu languages (Bislama, Nafsan, Nguna). We fine-tune models by recording session, comparing Character Error Rate trajectories when prioritising acoustic cleanliness versus linguistic richness. We demonstrate that prioritising lexically rich narratives and increasing acoustic-phonetic repetition, even in noisy environments, leads to faster improvements in transcription quality.

Problem

• language documentation
• automatic speech recognition
• audio transcription
• cold start problem
• field linguistics

Innovation

• no-code ASR pipeline
• iterative fine-tuning
• transcription prioritisation
• language documentation
• cold start problem