Overview
ABSTRACT
Hidden Markov models are an essential tool for the treatment, exploration, classification, labeling and clustering of sequential data and complex signals. They have been intensively used for tasks linked to the processing of signals and sequences conveying a linguistic message such as speech signal, write signal or text. They have also been used to process various types of other signals in bio-computing, navigation sequences and man-machine interaction.
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Thierry ARTIÈRES : Computer science teacher - Paris 6 Computer Science Laboratory (LIP6) - Pierre and Marie Curie University (UPMC)
INTRODUCTION
Markov models are a family of statistical models for the processing, analysis and classification of structured data. This article focuses on one instance of these models, Hidden Markov Models (HMMs), which have been and remain widely used in the classification and labeling of complex sequences and signals. They have been used extensively for tasks linked to the processing of signals and sequences conveying a linguistic message, such as speech signals. , the write signal , text. They have also been used to process various other types of signal in bioinformatics, navigation sequences and human-machine interaction, etc.
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Hidden Markov models for sequence labeling
Background, applications and modeling
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