Award Date

5-2011

Degree Type

Thesis

Degree Name

Master of Science in Computer Science

Department

Computer Science

First Committee Member

Kazem Taghva, chair

Second Committee Member

Ajoy K. Datta

Third Committee Member

Laxmi P. Gewali

Graduate Faculty Representative

Muthukumar Venkatesan

Number of Pages

67

Abstract

One of the most frequently used concepts applied to a variety of engineering and scientific studies over the recent years is that of a Hidden Markov Model (HMM). The Hidden semi-Markov model (HsMM) is contrived in such a way that it does not make any premise of constant or geometric distributions of a state duration. In other words, it allows the stochastic process to be a semi-Markov chain. Each state can have a collection of observations and the duration of each state is a variable. This allows the HsMM to be used extensively over a range of applications. Some of the most prominent work is done in speech recognition, gene prediction, and character recognition.

This thesis deals with the general structure and modeling of Hidden semi-Markov models and their implementations. It will further show the details of evaluation, decoding, and training with a running example.

Keywords

Abhinav thesis; Computer simulation; Hidden Markov model; Hidden semi-Markov model; HSMM; Implementation of HSMM; Markov processes; Mathematical models; Semi-Markov; Stochastic processes

Disciplines

Applied Mathematics | Computer Sciences | Theory and Algorithms

Language

English


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