This report explains the theory of Hidden Markov Models (HMMs). The emphasis is on the theory aspects in conjunction with the implementation issues that are encountered in a floating point processor. The main theory and implementation issues are based on the use of a Gaussian Mixture Model (GMM) as the state density in the HMM, and a Continuous Density Hidden Markov Model (CDHMM) is assumed. Su...

A Hidden Markov Model (HMM) Toolbox within the Matlab environment is presented. In this toolbox, the conventional techniques for the continuous and discrete HMM are developed for the training as well as for the test phases. The ability to make different groups of components for the vector pattern is provided. Multilabeling techniques for the discrete HMM is also provided. The toolbox includes p...

The concept of Recognition one phase of Speech Recognition Process using Hidden Markov Model has been discussed in this paper. Preprocessing, Feature Extraction and Recognition three steps and Hidden Markov Model (used in recognition phase) are used to complete Automatic Speech Recognition System. Today’s life human is able to interact with computer hardware and related machines in their own la...

Speech-input translation can be properly approached as a pattern recognition problem by means of statistical alignment models and stochastic finite-state transducers. Under this general framework, some specific models are presented. One of the features of such models is their capability of automatically learning from training examples. Moreover, the stochastic finite-state transducers permit an...

The enormous popularity of Hidden Markov models (HMMs) in spatio-temporal pattern recognition is largely due to the ability to “learn” model parameters from observation sequences through the Baum-Welch and other re-estimation procedures. In this study, HMM parameters are estimated from an ensemble of models trained on individual observation sequences. The proposed methods are shown to provide s...

In this paper an attempt is made to describe the various basic aspects of speech recognition system using the pattern recognition approach. Also here a new Autoregressive Hidden Markov Model (HMM) used for the speech recognition system has been introduced. Furthermore a viterbi Algorithm function is used as pattern matching technique for finding the best suited path. The main objective of this ...

The concept of Recognition one phase of Speech Recognition Process using Hidden Markov Model has been discussed in this paper. Preprocessing, Feature Extraction and Recognition three steps and Hidden Markov Model (used in recognition phase) are used to complete Automatic Speech Recognition System. Today’s life human is able to interact with computer hardware and related machines in their own la...