mlsanityData to 14 Oct 2026

Wav2Gloss: Generating Interlinear Glossed Text from Speech

arXiv 2403.13169 ↗First posted 19 Mar 2024cs.CLcited by 8

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Abstract (arXiv)

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.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 25 of its 65 references that are in the map. First posted on arXiv 19 Mar 2024.

Builds on

25 of 65 references.

References · 25

  1. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
  2. 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 4,057
  3. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,935
  4. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,905
  5. 2021HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Unitscited by 2,342
  6. 2021WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processingcited by 1,657
  7. 2020Gradient Surgery for Multi-Task Learningcited by 1,256
  8. 2020BLEURT: Learning Robust Metrics for Text Generationcited by 1,203
  9. 2021SUPERB: Speech processing Universal PERformance Benchmarkcited by 666
  10. 2021XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scalecited by 601
  11. 2018ESPnet: End-to-End Speech Processing Toolkitcited by 558
  12. 2021ByT5: Towards a token-free future with pre-trained byte-to-byte modelscited by 509
13 more
  1. 2016Joint CTC-Attention based End-to-End Speech Recognition using Multi-task Learningcited by 257
  2. 2023Seamless: Multilingual Expressive and Streaming Speech Translationcited by 192
  3. 2020Recent Developments on ESPnet Toolkit Boosted by Conformercited by 132
  4. 2024OWSM v3.1: Better and Faster Open Whisper-Style Speech Models based on E-Branchformercited by 74
  5. 2018Multilingual sequence-to-sequence speech recognition: architecture, transfer learning, and language modelingcited by 34
  6. 2023Improving Massively Multilingual ASR With Auxiliary CTC Objectivescited by 31
  7. 2022SUPERB @ SLT 2022: Challenge on Generalization and Efficiency of Self-Supervised Speech Representation Learningcited by 26
  8. 2023Comparison of Multilingual Self-Supervised and Weakly-Supervised Speech Pre-Training for Adaptation to Unseen Languagescited by 20
  9. 2020DARTS-ASR: Differentiable Architecture Search for Multilingual Speech Recognition and Adaptationcited by 15
  10. 2021Leveraging End-to-End ASR for Endangered Language Documentation: An Empirical Study on Yolox'ochitl Mixteccited by 15
  11. 2023Findings of the 2023 ML-SUPERB Challenge: Pre-Training and Evaluation over More Languages and Beyondcited by 10
  12. 2023Master-ASR: Achieving Multilingual Scalability and Low-Resource Adaptation in ASR with Modular Learningcited by 9
  13. 2022When Is TTS Augmentation Through a Pivot Language Useful?cited by 8

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 2; 2026: 4.

8 papers.

  1. 2025POWSM: A Phonetic Open Whisper-Style Speech Foundation Modelcited by 14
  2. 2025Interdisciplinary Research in Conversation: A Case Study in Computational Morphology for Language Documentationcited by 4
  3. 2026Massively Multilingual Joint Segmentation and Glossingcited by 1
  4. 2024ESPnet-EZ: Python-only ESPnet for Easy Fine-tuning and Integrationcited by 0
  5. 2024Boosting the Capabilities of Compact Models in Low-Data Contexts with Large Language Models and Retrieval-Augmented Generationcited by 0
  6. 2026Hybrid Neural-LLM Pipeline for Morphological Glossing in Endangered Language Documentation: A Case Study of Jungar Tuvancited by 0
  7. 2026CWoMP: Morpheme Representation Learning for Interlinear Glossingcited by 0
  8. 2026Phone Segmentation and Recognition through Phonological Activation Mappingcited by 0