Markov models | Dynamic programming | Error detection and correction

Viterbi algorithm

The Viterbi algorithm is a dynamic programming algorithm for obtaining the maximum a posteriori probability estimate of the most likely sequence of hidden states—called the Viterbi path—that results in a sequence of observed events, especially in the context of Markov information sources and hidden Markov models (HMM). The algorithm has found universal application in decoding the convolutional codes used in both CDMA and GSM digital cellular, dial-up modems, satellite, deep-space communications, and 802.11 wireless LANs. It is now also commonly used in speech recognition, speech synthesis, diarization, keyword spotting, computational linguistics, and bioinformatics. For example, in speech-to-text (speech recognition), the acoustic signal is treated as the observed sequence of events, and a string of text is considered to be the "hidden cause" of the acoustic signal. The Viterbi algorithm finds the most likely string of text given the acoustic signal. (Wikipedia).

Viterbi algorithm
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From playlist Natural Language Processing

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From playlist Machine Learning

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The Viterbi algorithm (computing the MAP sequence of hidden states) for hidden Markov models (HMMs).

From playlist Machine Learning

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From playlist Unboxing / Product Reviews

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From playlist Classical Cryptography

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From playlist Data Science

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Lec 15 | MIT 6.451 Principles of Digital Communication II

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From playlist MIT 6.451 Principles of Digital Communication II

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From playlist Workshop: Big Data in Multi-Messenger Astrophysics

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From playlist Workshop: Parametrized complexity and discrete optimization

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From playlist Numerical Analysis and Scientific Computing

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7. Viterbi decoding

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From playlist MIT 6.02 Introduction to EECS II: Digital Communication Systems, Fall 2012

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From playlist CMU Neural Nets for NLP 2017

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From playlist GRCon 2021

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From playlist How to Solve Multi Step Equations with Parenthesis

Related pages

Turbo code | Markov information source | Expectation–maximization algorithm | Maximum a posteriori estimation | Bayesian network | Graphical model | Iterative Viterbi decoding | Dynamic programming | Wagner–Fischer algorithm | Viterbi decoder | Godfried Toussaint | Baum–Welch algorithm | Trellis (graph) | Arg max | Markov random field | Hidden Markov model | Belief propagation | Likelihood function | Needleman–Wunsch algorithm | Forward algorithm | Markov chain | A* search algorithm | Stochastic matrix | Forward error correction | A priori probability | Part-of-speech tagging | Amortized analysis | Convolutional code | Conditional random field | State space | Algorithm