Chia-ying Lee

TL;DR: An unsupervised model is presented that simultaneously segments the speech, discovers a proper set of sub-word units and learns a Hidden Markov Model for each induced acoustic unit and outperforms a language-mismatched acoustic model.

TL;DR: It is shown that the model is competitive with state-of-the-art spoken term discovery systems, and analyses exploring the model’s behavior and the kinds of linguistic structures it learns are presented.

TL;DR: In this article, the authors summarize the accomplishments of a multi-disciplinary workshop exploring the computational and scientific issues surrounding zero resource speech technologies and related models of early language acquisition and present two strategies for integrating zero resource techniques into supervised settings, demonstrating the potential of unsupervised methods to improve mainstream technologies.

TL;DR: This paper documents efforts to deploy speech-enabled web interfaces to large audiences over the Internet via Amazon Mechanical Turk, an online marketplace for work, using the open source WAMI Toolkit.

TL;DR: This paper investigates how chil- dren and adults readily learn the spoken form of new words from one example – recognizing arbitrary instances of a novel phonological sequence, and excluding non-instances, regard- less of speaker identity and acoustic variability.