MA 490 / CSSE 490
Pattern Recognition

                      using Hidden Markov Models


What is an HMM?

Hidden Markov Models (HMMs) are the powerful pattern recognition algorithms used in speech-to-text and handwriting  recognition software. Rose-Hulman's automated telephone operator is an HMM. HMMs are currently being used to:

Why do HMMs work well in practice?

HMMs have a simple, but rich structure which enables them to be designed to take advantages of a particular application's special properties. HMM algorithms can be shown to be optimal, i.e. best in class.

What do I need to know to take this course?

MA223 Statistics or MA381 Probability provides sufficient background. All the other concepts needed will be developed in the course.

How will my grade be computed?

The goal of the course is to foster creativity and innovation. There will be no tests. Short quizzes/lessons will test comprehension of elementary concepts. A significant portion of the course will involve working on individual or group projects chosen by students.

  • 30% Homework
  • 30% Quizzes/Lessons
  • 30% Project
  • 10% Class Participation

Example (Dishonest Casino)

The diagram below is a simple HMM of a dishonest casino which switches secretly between fair and unfair dice.  Fair and unfair die rolls are hidden states.  


The only observable quantities are the die rolls b1(k) and b2(k). For example, consider the die rolls:

4, 3, 5, 7, 8, 9, 6, 6, 6, 6, 3, 4, 5, 6, 6,  2, 5, 3, 4, 6, 1, 2, 1

Using only the above sequence of die rolls, an HMM attempts to determine which rolls were done using a fair dice and which were done using an unfair dice.

Past Student Projects

  • Biology
    Stochastic Context-Free Grammars and tRNA Folding,
    by Michael Ewing, Mike Simon and Phil Smith.
    Using HMMs to Predict Secondary Structure in Proteins
    by Charles McAnany
  • Robotics
    Mobile Robot Localization using Kalman Filters,
    by Jon Klein and Trenton Tabor.
    Textured Image Segmentation Using Wavelet-Domain Hidden Markov Trees, by Jay Groven and Alex Van Brunt.
    Matching Pixels in Rectified Stereo Images Using Hidden Markov Models, by Adam Thomas and Matthew Stachawski.
  • Chemical Engineering
    Determination of Process States with Hidden Markov Models,
    by Joe Kelly.
  • Economics
    Structural Macroeconomics Analysis of the Business Cycle Using Hidden Markov Models with Continuous Emitted Symbols, by Nicholas McKinney.
    Exploring the Connections Between Economic Indicators,
    by Aaron Knox.
    Class Distinctions of a Simplified Economic Model,
    by David McGinnis, David Loughry and Greg Jackson.
  • Data Mining
    Paragraph Keyword Acquisition,
    by Bryan Shell.
    Chronological Typewriting Analysis,
    by Troy Reilly.
    Sentence Structure Validator
    by Devin Banks.
    Using N-Gram Similarity Metrics for Relation Clustering
    by Stephen Mayhew and Nicholas Kamper.
  • Music
    Smart DJ,
    by Peter Witon.
    Classification of Music by Genre
    by Matthew Oelschlaegar, Mathew Mercer, Arada Tugay and Zachary Stewart.
    Genre Classification by Chord Progression
    by Michael Eaton and Samuel Kim
  • Astronomy
    Exoplanet Detection via HMM Analysis
    by Jon Drobny.
  • Image Processing
    Food Image Processing, An Approach using Markov Modeling
    by Rob Wagner and Alex Petitjean.
    Where's Waldo (Facial Recognition)
    Chris Gropp and Nicole Richardson.
  • Misc.
    The Application of HMMs in Security
    by Nathan Catt and Alex Jacoby

    Acoustic Crytanalysis of Keyboard Emissions through HMMs
    by Ross Hansen and Elias White.
    Detecting Collusion in Multiplayer Games
    by Dong Lee and Alec Manke.


Yosi Shibberu, Rose-Hulman Institute of Technology
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