Visualizing States in Hidden Markov Models using SOHMM

Hidden Markov Models with Mixed States

We note similarities of the state space reconstruction (\Embedology") practiced in numerical work on chaos, state space methods of stochastic systems theory, and the hidden Markov models (HMMs) used in speech research. We review Baum's EM algorithm in general and the speciic forward-backward algorithm that optimizes a class of HMM that has a mixed state space consisting of continuous and discre...

Basecalling using hidden Markov models

In this paper we propose hidden Markov models to model electropherograms from DNA sequencing equipment and perform basecalling. We model the state emission densities using artificial neural networks, and modify the Baum–Welch reestimation procedure to perform training. Moreover, we develop a method that exploits consensus sequences to label training data, thus minimizing the need for hand label...

Locating Hidden Groups in Communication Networks Using Hidden Markov Models

A communication network is a collection of social groups that communicate via an underlying communication medium (for example newsgroups over the Internet). In such a network, a hidden group may try to camoflauge its communications amongst the typical communications of the network. We study the task of detecting such hidden groups given only the history of the communications for the entire comm...

Intrusion Detection Using Evolutionary Hidden Markov Model

Intrusion detection systems are responsible for diagnosing and detecting any unauthorized use of the system, exploitation or destruction, which is able to prevent cyber-attacks using the network package analysis. one of the major challenges in the use of these tools is lack of educational patterns of attacks on the part of the engine analysis; engine failure that caused the complete training,  ...

Speaker Independent Speech Recognition Using Hidden Markov Models for Persian Isolated Words