Discovering linguistic structures in speech : models and applications
Author(s)Lee, Chia-ying (Chia-ying Jackie)
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
James R. Glass.
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The ability to infer linguistic structures from noisy speech streams seems to be an innate human capability. However, reproducing the same ability in machines has remained a challenging task. In this thesis, we address this task, and develop a class of probabilistic models that discover the latent linguistic structures of a language directly from acoustic signals. In particular, we explore a nonparametric Bayesian framework for automatically acquiring a phone-like inventory of a language. In addition, we integrate our phone discovery model with adaptor grammars, a nonparametric Bayesian extension of probabilistic context-free grammars, to induce hierarchical linguistic structures, including sub-word and word-like units, directly from speech signals. When tested on a variety of speech corpora containing different acoustic conditions, domains, and languages, these models consistently demonstrate an ability to learn highly meaningful linguistic structures. In addition to learning sub-word and word-like units, we apply these models to the problem of one-shot learning tasks for spoken words, and our results confirm the importance of inducing intrinsic speech structures for learning spoken words from just one or a few examples. We also show that by leveraging the linguistic units our models discover, we can automatically infer the hidden coding scheme between the written and spoken forms of a language from a transcribed speech corpus. Learning such a coding scheme enables us to develop a completely data-driven approach to creating a pronunciation dictionary for the basis of phone-based speech recognition. This approach contrasts sharply with the typical method of creating such a dictionary by human experts, which can be a time-consuming and expensive endeavor. Our experiments show that automatically derived lexicons allow us to build speech recognizers that consistently perform closely to supervised speech recognizers, which should enable more rapid development of speech recognition capability for low-resource languages.
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.Cataloged from PDF version of thesis.Includes bibliographical references (pages 169-188).
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology
Electrical Engineering and Computer Science.