Autoregressive hidden Markov models and the speech signal

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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Bryan, Jacob

Contributors dc:contributor

• Levinson, Stephen E.

• language acquisition
• Hidden Markov Model (HMM)
• linear prediction
• speech
• signal processing
• linear prediction coefficient (LPC)

Statement dc:rights

• Copyright 2014 Jacob Bryan

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/72994

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/72994