Dymarski P. (Ed.). Hidden Markov Models, Theory and Applications

InTech, 2011. - 324 p. - This book presents theoretical issues and a variety of HMMs applications in speech recognition and synthesis, medicine, neurosciences, computational biology, bioinformatics, seismology, environment protection and engineering. The reader will find this book useful and helpful for their own research. Hidden Markov Models (HMMs), although known for decades, have made a big career nowadays and are still in state of development.Tutorials and Theoretical Issues
History and Theoretical Basics of Hidden Markov Models
Hidden Markov Models in Dynamic System Modelling and Diagnosis
Theory of Segmentation
Classification of Hidden Markov Models: Obtaining Bounds on the Probability of Error and Dealing with Possibly Corrupted Observations
Hidden Markov Models in Speech and Time-domain Signals Processing
Hierarchical Command Recognition Based on Large Margin Hidden Markov Models
Modeling of Speech Parameter Sequence Considering Global Variance for HMM-Based Speech Synthesis
Using Hidden Markov Models for ECG Characterisation
Hidden Markov Models in the Neurosciences
Volcano-Seismic Signal Detection and Classification Processing Using Hidden Markov Models - Application to San Cristobal and Telica Volcanoes. Nicaragua
Non-Homogeneous Hidden Markov Model for the Analysis of Multi-Pollutant Exceedances Data
Hidden Markov Models in Image and Spatial Structures Analysis
Continuous Hidden Markov Models for Depth Map-Based Human Activity Recognition
Applications of Hidden Markov Models in Microarray Gene Expression Data
Application of HMM to the Study of Three-Dimensional Protein Structure
Control Theoretic Approach to Platform Optimization using HMM