Thousands of the world's languages are in danger of extinction--a tremendous threat to cultural identities and human language diversity. Interlinear Glossed Text (IGT) is a form of linguistic annotation that can support documentation and resource creation for these languages' communities. IGT typically consists of (1) transcriptions, (2) morphological segmentation, (3) glosses, and (4) free translations to a majority language. We propose Wav2Gloss: a task in which these four annotation components are extracted automatically from speech, and introduce the first dataset to this end, Fieldwork: a corpus of speech with all these annotations, derived from the work of field linguists, covering 37 languages, with standard formatting, and train/dev/test splits. We provide various baselines to lay the groundwork for future research on IGT generation from speech, such as end-to-end versus cascaded, monolingual versus multilingual, and single-task versus multi-task approaches.
@article{arxiv.2403.13169,
title = {Wav2Gloss: Generating Interlinear Glossed Text from Speech},
author = {Taiqi He and Kwanghee Choi and Lindia Tjuatja and Nathaniel R. Robinson and Jiatong Shi and Shinji Watanabe and Graham Neubig and David R. Mortensen and Lori Levin},
journal= {arXiv preprint arXiv:2403.13169},
year = {2024}
}