Autoregressive hidden Markov models and the speech signal
Bryan, Jacob
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Jacob_Bryan.pdf
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https://hdl.handle.net/2142/72994
Description
Title
Autoregressive hidden Markov models and the speech signal
Author(s)
Bryan, Jacob
Issue Date
2015-01-21
Director of Research (if dissertation) or Advisor (if thesis)
Levinson, Stephen E.
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Date of Ingest
2015-01-21T19:55:27Z
Keyword(s)
language acquisition
Hidden Markov Model (HMM)
linear prediction
speech
signal processing
linear prediction coefficient (LPC)
Abstract
This thesis introduces an autoregressive hidden Markov model (HMM) and demonstrates its application to the speech signal. This new variant of the HMM is built upon the mathematical structure of the HMM and linear prediction analysis of speech signals. By incorporating these two methods into one inference algorithm, linguistic structures are inferred from a given set of speech data. These results extend historic experiments in which the HMM is used to infer linguistic information from text-based information and from the speech signal directly. Given the added robustness of this new model, the autoregressive HMM is suggested as a starting point for unsupervised learning of speech recognition and synthesis in pursuit of modeling the process of language acquisition.
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Jacob_Bryan.pdf
