6 hmm by kannan

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6 hmm by kannan

  1. 1. Hidden Markov Models By Maravar Kannan
  2. 2. Markov Chain• A Markov chain named after Andrey Markov, is a mathematical system that undergoes transitions from one state to another, between a finite or countable number of possible states.• It is a random process usually characterized as memoryless:• the next state depends only on the current state and not on the sequence of events that preceded it.
  3. 3. Discrete –Time Markov Process• Discrete –Time Markov Process (discrete-time Markov chain or DTMC) is When a Markov Chain result is considered at a finite interval.• What is the probality that the weather for 8 consecutive days is “Sun-Sun-Sun-Rain-Rain-Sun-Cloudy-Sun” 3
  4. 4. Discrete –Time Markov Process•
  5. 5. Extension to Hidden Markov Model• Now, we extend the concept of Markov models to include the case in which the observation is a probabilistic function of the state-that is, the resulting model (which is Hidden Markov model) is a doubly embedded stochastic process with an underlying stochastic process that is not 5 directly Observed only through
  6. 6. Extension to Hidden Markov Model2004/11/16 6
  7. 7. The Urn-and-Ball Model2004/11/16 7
  8. 8. Elements of an HMM 8
  9. 9. Types of HMMs 9
  10. 10. Implementation Issues for HMMs Scaling Multiple Observation Sequences Initial Estimates of HMM Parameters Effect of Insufficient Training Data Choice of Model 10
  11. 11. Conclusion• The conclusion of this study of recognition and hidden markov model has been carried out to develop a voice based user machine interface system. In various applications we can use this user machine system and can take advantages as real interface, these application can be related with disable persons those are unable to operate computer through keyboard and mouse, these type of persons can use computer with the use of Automatic Speech Recognition system, with this system user can operate computer with their own voice
  12. 12. Thank You 12

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