Information theory is applied to the problem of multi-aspect, time-domain scattering from a general target. The statistical variation of the scattered fields with variable target-sensor orientation is characterized via a hidden Markov model (HMM), a state of which corresponds to a generally contiguous set of target-sensor orientations over which the angular-dependent scattered fields are statio...

The performance of multiple hypothesis testing is known to be affected by the statistical dependence among random variables involved. The mechanisms responsible for this, however, are not well understood. We study the effects of the dependence structure of a finite state hidden Markov model (HMM) on the likelihood ratios critical for optimal multiple testing on the hidden states. Various conver...

Hidden Markov models (HMM) are well known in speech recognition, where they are trained to recognize spoken words and even whole sentences. They are used to find the parameters of a so-called hidden model (usually a DTMC) by training it with observed output sequences. This paper introduces an approach to train stochastic Petri nets with the methods of HMM. As opposed to a DTMC, a stochastic Pet...

A new hidden Markov model (HMM) for the space-time evolution of daily rainfall is developed which models precipitation within hidden regional weather types by censored power-transformed Gaussian distributions. The latter provide flexible and interpretable multivariate models for the mixed discrete-continuous variables that describe both precipitation, when it occurs, and no precipitation. The m...

MOTIVATION Score-based progressive alignment algorithms do dynamic programming on successive branches of a guide tree. The analogous probabilistic construct is an Evolutionary HMM. This is a multiple-sequence hidden Markov model (HMM) made by combining transducers (conditionally normalised Pair HMMs) on the branches of a phylogenetic tree. METHODS We present general algorithms for constructin...

We propose a storage workload model able to process discrete time series incrementally, continually updating its parameters with the availability of new data. More specifically, a Hidden Markov Model (HMM) with an adaptive Baum-Welch algorithm is trained on two raw traces: a NetApp network trace consisting of timestamped I/O commands and a Microsoft trace also with timestamped entries containin...

In this paper, a new Hidden Markov Model (HMM)-based face recognition system is proposed. As a novel point despite of five-state HMM used in pervious researches, we used 7-state HMM to cover more details. Indeed we add two new face regions, eyebrows and chin, to the model. As another novel point, we used a small number of quantized Singular Values Decomposition (SVD) coefficients as feature...

This paper describes a new scheme for robust speech recognition systems where visual information and acoustic features are merged. Using as robust unit the « pseudo-diphone », we compare a global Hidden Markov Model (HMM) and a Master/Slave HMM through a centisecond preprocessing and through a segmental one. We confirm by experimentation the importance of articulatory features in clean and nois...

In this paper, we introduce an approach to improve the recognition performance of a Hidden Markov Model (HMM) based monophone recognizer using Support Vector Machines (SVMs). We developed and examined a method for re-scoring the HMM recognizer hypotheses by SVMs in a phoneme recognition framework. Compared to a stand-alone HMM system, an improvement of 9.2% was reached on the TIMIT database and...