Hidden Markov Models
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Introduction
The following presentation is adapted from [Rabiner & Juang, 1986] and [Charniak, 1993].
Notational conventions ...
Hidden Markov Models
Tutorial Slides by Andrew Moore
In this tutorial we'll begin by reviewing Markov Models (aka Markov Chains) and then...we'll hide them! This simulates a very common phenomenon...
Hidden Markov model - Definition
A hidden Markov model (HMM) is a statistical model where the system being modelled is assumed to be a Markov process with unknown parameters, and the challenge is to determine the hidden parameters, ...
Hidden Markov models are especially known for their application in temporal pattern recognition such as speech, handwriting, gesture recognition, part-of-speech tagging, musical score following, partial discharges and bioinformatics.
Hidden Markov Model A joint statistical model for an ordered sequence of variables. It is the result of stochastically perturbing the variables in a Markov chain (the original variables are thus "hidden").
Hidden Markov Model for Biological Sequence Analysis
Markov Chain
A Markov chain is a model that generates sequences in which the probability of a symbol depends only on the previous symbol. Figure below is an example Markov chain model.
Hidden Markov Models Tutorial from the School of Computing, University of Leeds. "Often we are interested in finding patterns which appear over a space of time.
6.2 The Hidden Markov Model
6.3 Likelihood Computation: The Forward Algorithm
6.4 Decoding: The Viterbi Algorithm ...
Several successful algorithms have been developed for filtering, prediction, smoothing and finding explanations for streams of data,[99] such as hidden Markov models,[100] Kalman filters[101] and dynamic Bayesian networks.
Another example: Most voice-recognition systems are based on a mathematical theory called Hidden Markov Models. Consider the following argument: ``If a computer recognizes words using Hidden Markov Models, then it doesn't recognize words the way I do.
However, the most common method is to use a Hidden Markov Model (HMM). The theory behind HMMs is complicated, but a brief look at simple Markov Models will help you gain an understanding of how they work.
Computational linguistics
Digital signal processing
Dynamic time warping
Hidden Markov models
Linguistics
Mondegreen
Mel Frequency Cepstral Coefficients (MFCCs)
Pattern recognition
Voice analysis
Voice command device ...
Designers of OCR programs may choose NNWs to accomplish one or more of these steps with NNWs while using for other steps other techniques such as conventional AI (If-Then rules), statistical models, hidden Markov models, etc.
Each output of the network represents the probability of a specific phone (speech sound, e.g. /i/, /p/, etc), given both present and recent input. The probabilities are then interpreted by a Hidden Markov Model which tries to recognize the whole ...
 See also: Machine learning, Neural network, Knowledge, Artificial intelligence, Inference
  
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