Unsupervised Lexicon Discovery from Acoustic Input
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Author(s)
Lee, Chia-ying
O'Donnell, Timothy John
Glass, James R.
Date Issued
July 2015
Journal
Transactions of the Association for Computational Linguistics
Publisher
Association for Computational Linguistics
Citation
Lee, Chia-ying, Timothy J. O'Donnell, and James Glass. "Unsupervised Lexicon Discovery from Acoustic Input." Transactions of the Association for Computational Linguistics, Volume 3 (2015). © 2015 Association for Computational Linguistics
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://tacl2013.cs.columbia.edu/ojs/index.php/tacl/article/view/520
