Hidden Markov Model September 21, 2007
Posted by nhabibi in Artificial Intelligence, Pattern Recognition.1 comment so far
A Hidden Markov Model (HMM) is a statistical model in which the system being modeled is assumed to be a Markov process with unknown parameters, and the challenge is to determine the hidden parameters from the observable parameters. The extracted model parameters can then be used to perform further analysis, for example for pattern recognition applications.
In a regular Markov model, the state is directly visible to the observer, and therefore the state transition probabilities are the only parameters. In a hidden Markov model, the state is not directly visible, but variables influenced by the state are visible. Each state has a probability distribution over the possible output tokens. Therefore the sequence of tokens generated by an HMM gives some information about the sequence of states.
HMM has application in many fields that need to recognize patterns statistically, such as speech recognition, optical character recognition, cryptanalysis, boinformatics, etc.
I ignore describing Hidden Markov Model’s mathematic details here. My report, in Persian, introduces HMM in detail and describes its application in Speech Recognition briefly.
More information:
- A good tutorial from Leeds university
- L. R. Rabiner’s paper on Hidden Markov Model
- HMM Wikipedia page
