@article{lee-etal-2015-unsupervised,
    title = "Unsupervised Lexicon Discovery from Acoustic Input",
    author = "Lee, Chia-ying  and
      O{'}Donnell, Timothy J.  and
      Glass, James",
    editor = "Collins, Michael  and
      Lee, Lillian",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "3",
    year = "2015",
    address = "Cambridge, MA",
    publisher = "MIT Press",
    url = "https://preview.aclanthology.org/ingest-emnlp/Q15-1028/",
    doi = "10.1162/tacl_a_00146",
    pages = "389--403",
    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."
}