Wav2Gloss: Generating Interlinear Glossed Text from Speech
This addresses the need for efficient linguistic annotation to preserve endangered languages, though it is incremental as it builds on existing annotation methods.
The paper tackles the problem of automatically generating Interlinear Glossed Text (IGT) from speech to support endangered language documentation, and introduces the first dataset, Fieldwork, covering 37 languages with baseline models for this task.
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.