Unsupervised Lexicon Discovery from Acoustic Input

Chia-ying Lee, Timothy J. O’Donnell, James Glass


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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/Q15-1028.pdf