Linguistic unit discovery from multi-modal inputs in unwritten languages: Summary of the "Speaking Rosetta" JSALT 2017 Workshop
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Abstract (arXiv)
We summarize the accomplishments of a multi-disciplinary workshop exploring the computational and scientific issues surrounding the discovery of linguistic units (subwords and words) in a language without orthography. We study the replacement of orthographic transcriptions by images and/or translated text in a well-resourced language to help unsupervised discovery from raw speech.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Feb 2018.
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Citing papers per year: 2022: 4.
4 papers.
- 2022Self-Supervised Speech Representation Learning: A Reviewcited by 258
- 2022Self-supervised language learning from raw audio: Lessons from the Zero Resource Speech Challengecited by 34
- 2022Word Segmentation on Discovered Phone Units with Dynamic Programming and Self-Supervised Scoringcited by 24
- 2022Keyword localisation in untranscribed speech using visually grounded speech modelscited by 7
- 2022Self-supervised language learning from raw audio: Lessons from the Zero Resource Speech Challenge27 Oct 2022
- 2022Self-Supervised Speech Representation Learning: A Review21 May 2022
- 2022Word Segmentation on Discovered Phone Units with Dynamic Programming and Self-Supervised Scoring24 Feb 2022
- 2022Keyword localisation in untranscribed speech using visually grounded speech models2 Feb 2022