HiddenMarkovModels-ATutorialfortheCourseComputationalIntelligence.pdfVIP

Hidden Markov Models A Tutorial for the Course Computational Intelligence http://www.igi.tugraz.at/lehre/CI Barbara Resch (modified by Erhard and Car line Rank) Signal Processing and Speech Communication Laboratory Inffeldgasse 16c/II phone 873–4436 Abstract This tutorial gives a gentle introduction to Markov models and hidden Markov models (HMMs) and relates them to their use in automatic speech recognition. Additionally, the Viterbi algorithm is considered, relating the most likely state sequence of a HMM to a given sequence of observations. Usage To make full use of this tutorial you should 1. Download the file HMM.zip1 which contains this tutorial and the accompanying Matlab programs. 2. Unzip HMM.zip which will generate a subdirectory named HMM/matlab where you can find all the Matlab programs. 3. Add the folder HMM/matlab and the subfolders to the Matlab search path with a command like addpath(’C:\Work\HMM\matlab’) if you are using a Windows machine or addpath(’/home/- jack/HMM/matlab’) if you are using a Unix/Linux machine. Sources This tutorial is based on • “Markov Models and Hidden Markov Models - A Brief Tutorial” International Computer Science Institute Technical Report TR-98-041, by Eric Fosler-Lussier, • EPFL lab notes “Introduction to Hidden Markov Models” by Herv´e Bourlard, Sacha Krstulovi´c, and Mathew Magimai-Doss, and • HMM-Toolbox (also included in BayesNet Toolbox) for Matlab by Kevin Murphy. 1 Markov Models Let’s talk about the weather. Let’s say in Graz, there are three types of weather: sunny , rainy , and foggy . Let’s assume for the moment