Abstract
Automated extraction methods are widely available for vowels, but automated methods for coding rhoticity have lagged far behind. R-fulness versus r-lessness (in words like park, store, etc.) is a classic and frequently cited variable, but it is still commonly coded by human analysts rather than automated methods. Human-coding requires extensive resources and lacks replicability, making it difficult to compare large datasets across research groups. Can reliable automated methods be developed to aid in coding rhoticity? In this study, we use Neural Networks/Deep Learning, training our model on 208 Boston-area speakers.- Anthology ID:
- N19-3013
- Volume:
- Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 92–96
- Language:
- URL:
- https://aclanthology.org/N19-3013
- DOI:
- 10.18653/v1/N19-3013
- Cite (ACL):
- Sarah Gupta and Anthony DiPadova. 2019. Deep Learning and Sociophonetics: Automatic Coding of Rhoticity Using Neural Networks. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop, pages 92–96, Minneapolis, Minnesota. Association for Computational Linguistics.
- Cite (Informal):
- Deep Learning and Sociophonetics: Automatic Coding of Rhoticity Using Neural Networks (Gupta & DiPadova, NAACL 2019)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/N19-3013.pdf