Hidden Markov Models (HMMs) are a set of statistical models used to characterize the statistical properties of a signal. An HMM is a doubly stochastic process with an underlying stochastic process that is not observable, but can be observed through another set of stochastic processes that produce a sequence of observed symbols. An HMM has a finite set of states, each of which is associated with...

Recent developments in the theory and uses of Bayesian Networks in pattern recognition and image understanding (PRIU) raise questions about the relationships between Bayesian compared to non-Bayesian approaches. In this paper we compare Neural-based verses Bayesian-based methods for PRIU. We conclude with the view that a singular PRIU architecture that models “from pixels to predicates” in one ...

Semi-continuous Hidden Markov Models (SCHMM) with gaussian distributions are often used in continuous speech or handwriting recognition systems. Our paper compares gaussian and tree-structured polynomial classi ers which have been successfully used in pattern recognition since many years. In our system the binary classi er tree is generated by clustering HMM states using an entropy measure. For...

Speech is inherently bimodal, relying on cues from the acoustic and visual speech modalities for perception. The McGurk effect demonstrates that when humans are presented with conflicting acoustic and visual stimuli, the perceived sound may not exist in either modality. This effect has formed the basis for modelling the complementary nature of acoustic and visual speech by encapsulating them in...

In HMM-based speech recognition, estimation of parameters of HMMs is viewed as counterpart of training or learning in traditional sequential pattern recognition since speech signal can be represented by a sequence of ndimension vectors after features are extracted from the speech signal. However, due to variation of duration of the phone with speakers and context and its randomness, speech samp...

This paper focus on a new approach to automatic speech recognition in noisy environments where the noise has either stationary or non-stationary statistical characteristics. The aim is to perform automatic recognition of speech in the precence of additive car noise. The technique applied is based on a combination of the Hidden Markov Model (HMM) decomposition method [ 11, for speech recognition...

Markov random "eld (MRF) modeling is a popular pattern analysis method and MRF parameter estimation plays an important role in MRF modeling. In this paper, a method based on Markov Chain Monte Carlo (MCMC) is proposed to estimate MRF parameters. Pseudo-likelihood is used to represent likelihood function and it gives a good estimation result. ( 2000 Pattern Recognition Society. Published by Else...

This paper presents recent development on text-dependent speaker verification technology in EU project PICASSO, which have improved the SV performance significantly. In the project we adopt HMM approach for pattern matching. In the paper we describes four different techniques, adaptive variance flooring, multiple use of enrolment sample, generalised competitive measurement for score normalisati...

The problem of signature verification is in theory a pattern recognition task used to discriminate two classes, original and forgery signatures. Even after many efforts in order to develop new verification techniques for static signature verification, the influence of the forgery types has not been extensively studied. This paper reports the contribution to signature verification considering di...