Massively Multilingual Adversarial Speech Recognition
Oliver Adams, Matthew Wiesner, Shinji Watanabe, David Yarowsky
Abstract
We report on adaptation of multilingual end-to-end speech recognition models trained on as many as 100 languages. Our findings shed light on the relative importance of similarity between the target and pretraining languages along the dimensions of phonetics, phonology, language family, geographical location, and orthography. In this context, experiments demonstrate the effectiveness of two additional pretraining objectives in encouraging language-independent encoder representations: a context-independent phoneme objective paired with a language-adversarial classification objective.- Anthology ID:
- N19-1009
- Volume:
- Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota
- Editors:
- Jill Burstein, Christy Doran, Thamar Solorio
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 96–108
- Language:
- URL:
- https://aclanthology.org/N19-1009
- DOI:
- 10.18653/v1/N19-1009
- Cite (ACL):
- Oliver Adams, Matthew Wiesner, Shinji Watanabe, and David Yarowsky. 2019. Massively Multilingual Adversarial Speech Recognition. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 96–108, Minneapolis, Minnesota. Association for Computational Linguistics.
- Cite (Informal):
- Massively Multilingual Adversarial Speech Recognition (Adams et al., NAACL 2019)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/N19-1009.pdf