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hidden markov model

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

2. HMM Overview Machine learning method 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) State machine:

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 Weather Once each day weather is observed State 1: rain State 2: cloudy State 3: sunny 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 State transition matrix Rainy Cloudy Sunny Rainy 0.4 0.3 0.3 Cloudy 0.2 0.6 0.2 Sunny 0.1 0.1 0.8

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!