First Order Hidden Markov Model - Theory and Implementation Issues

Document type: Researchreports
Full text:
Author(s): Mikael Nilsson
Title: First Order Hidden Markov Model - Theory and Implementation Issues
Translated title: Första Ordningens Gömda Markov Kedjor - Teori och Implementerings Aspekter
Series: Research Report
Year: 2005
Issue: 2
Editor: Mattias Dahl, Ingvar Claesson
ISSN: 1103-1581
Organization: Blekinge Institute of Technology
Department: (*** Master error ***)
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+46 455 38 50 00
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Authors e-mail:
Language: English
Abstract: This report explains the theory of Hidden Markov Models (HMMs). The emphasis is on the theory aspects in conjunction with the implementation issues that are encountered in a floating point processor. The main theory and implementation issues are based on the use of a Gaussian Mixture Model (GMM) as the state density in the HMM, and a Continuous Density Hidden Markov Model (CDHMM) is assumed. Suggestions and advice related to the implementation
are given for a typical pattern recognition task.
Subject: Signal Processing\Detection and Classification
Mathematics\Probability and Statistics
Signal Processing\General
Keywords: HMM, GMM, SOFM, k-means, Baum-Welch, Viterbi, Pattern Recognition
URN: urn:nbn:se:bth-00271