hidden markov model

1. Hidden Markov Model
Presented
By
Om Prakash Mahato
059/MSCKE/069
IOE Pulchowk Campus

2. HMM Overview
• Machine learning method
State machine:
• Makes use of state machines
• Based on probabilistic models
• Useful in problems having sequential steps
• Can only observe output from states, not the
states themselves
– Example: speech recognition
• Observe: acoustic signals
• Hidden States: phonemes
(distinctive sounds of a language)

3. Observable Markov Model

4. HMM Components
• A set of states (x’s)
• A set of possible output symbols (y’s)
• A state transition matrix (a’s)
– probability of making transition from
one state to the next
• Output emission matrix (b’s)
– probability of a emitting/observing a
symbol at a particular state
• Initial probability vector
– probability of starting at a particular
state
– Not shown, sometimes assumed to be
1

5. THE HIDDEN MARKOV MODEL DEFINITIONS

6. Observable Markov Model Example
State transition matrix
• Weather Rainy Cloudy Sunny
– Once each day weather is observed Rainy 0.4 0.3 0.3
• State 1: rain Cloudy 0.2 0.6 0.2
• State 2: cloudy
• State 3: sunny Sunny 0.1 0.1 0.8
– What is the probability the weather
for the next 7 days will be:
• sun, sun, rain, rain, sun, cloudy, sun
– Each state corresponds to a physical
observable event

7. Hidden Markov Model Example
• Coin toss:
– Heads, tails sequence with 2 coins
– You are in a room, with a wall
– Person behind wall flips coin, tells result
• Coin selection and toss is hidden
• Cannot observe events, only output (heads, tails) from
events
– Problem is then to build a model to explain
observed sequence of heads and tails

8. HMM Uses
• Uses
– Speech recognition
• Recognizing spoken words and phrases
– Text processing
• Parsing raw records into structured records
– Bioinformatics
• Protein sequence prediction
– Financial
• Stock market forecasts (price pattern prediction)
• Comparison shopping services

9. HMM Advantages / Disadvantages
• Advantages
– Effective
– Can handle variations in record structure
• Optional fields
• Varying field ordering
• Disadvantages
– Requires training using annotated data
• Not completely automatic
• May require manual markup
• Size of training data may be an issue

10. References
•Rabiner, L. R. (1989). A Tutorial on Hidden Markov Models and
Selected Applications in Speech Recognition. Proceedings of the
IEEE
•http://en.wikipedia.org/wiki/Hidden_Markov_model
•http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2766791/

11. Thank you!