Hidden Markov Model Presented By Om Prakash Mahato 059/MSCKE/069 IOE Pulchowk Campus
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)
Observable Markov Model
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
THE HIDDEN MARKOV MODEL DEFINITIONS
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
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
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
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
References•Rabiner, L. R. (1989). A Tutorial on Hidden Markov Models andSelected Applications in Speech Recognition. Proceedings of theIEEE•http://en.wikipedia.org/wiki/Hidden_Markov_model•http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2766791/
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