1. Hidden Markov
Models
By
Maravar Kannan

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. 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”
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4. Discrete –Time Markov
Process
•

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
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directly Observed only through

6. Extension to Hidden Markov
Model
2004/11/16 6

7. The Urn-and-Ball Model
2004/11/16 7

8. Elements of an HMM
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9. Types of HMMs
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10. Implementation Issues for
HMMs
Scaling
Multiple Observation
Sequences
Initial Estimates of HMM
Parameters
Effect of Insufficient
Training Data
Choice of Model
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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. Thank You
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