Abstract
We present a model of unsupervised phonological lexicon discovery—the problem of simultaneously learning phoneme-like and word-like units from acoustic input. Our model builds on earlier models of unsupervised phone-like unit discovery from acoustic data (Lee and Glass, 2012), and unsupervised symbolic lexicon discovery using the Adaptor Grammar framework (Johnson et al., 2006), integrating these earlier approaches using a probabilistic model of phonological variation. We show that the model is competitive with state-of-the-art spoken term discovery systems, and present analyses exploring the model’s behavior and the kinds of linguistic structures it learns.- Anthology ID:
- Q15-1028
- Volume:
- Transactions of the Association for Computational Linguistics, Volume 3
- Month:
- Year:
- 2015
- Address:
- Cambridge, MA
- Editors:
- Michael Collins, Lillian Lee
- Venue:
- TACL
- SIG:
- Publisher:
- MIT Press
- Note:
- Pages:
- 389–403
- Language:
- URL:
- https://aclanthology.org/Q15-1028
- DOI:
- 10.1162/tacl_a_00146
- Cite (ACL):
- Chia-ying Lee, Timothy J. O’Donnell, and James Glass. 2015. Unsupervised Lexicon Discovery from Acoustic Input. Transactions of the Association for Computational Linguistics, 3:389–403.
- Cite (Informal):
- Unsupervised Lexicon Discovery from Acoustic Input (Lee et al., TACL 2015)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/Q15-1028.pdf