Hidden Markov modeling (HMM) techniques have been applied in the past few years to characterize single ion channel current events at low signal-to-noise ratios (SNR’s). In this paper, an adaptation of the forward-backward procedure and Baum–Welch algorithm is presented to model ion channel kinetics under conditions of correlated and state-dependent excess noise like that observed in patch-clamp...

Hidden Markov models (HMM’s) are popular in many applications, such as automatic speech recognition, control theory, biology, communication theory over channels with bursts of errors, queueing theory, and many others. Therefore, it is important to have robust and fast methods for fitting HMM’s to experimental data (training). Standard statistical methods of maximum likelihood parameter estimati...

Hidden Markov Models (HMM) are used in a wide range of artifificial intelligence applications including speech recognition, computer vision, computational biology and fifinance. Estimating an HMM parameters is often addressed via the Baum-Welch algorithm (BWA), but this tends to convergence local optimum model parameters. Therefore, optimizing remains crucial challenging work. In paper, Variabl...

The use of segment-based features and segmentation networks in a segment-based speech recognizer complicates the probabilistic modeling because it alters the sample space of all possible segmentation paths and the feature observation space. This paper describes a novel Baum-Welch training algorithm for segment-based speech recognition which addresses these issues by an innovative use of finite-...

We derive the Baum-Welch algorithm for hidden Markov models (HMMs) through an information-theoretical approach using cross-entropy instead of the Lagrange multiplier approach which is universal in machine learning literature. The proposed approach provides a more concise derivation of the Baum-Welch method and naturally generalizes to multiple observations. Introduction The basic hidden Markov ...

This paper presents a new method of feature dimension reduction in hidden Markov modeling (HMM) for speech recognition. The key idea is to apply reduced rank maximum likelihood estimation in the M-step of the usual Baum-Welch algorithm for estimating HMM parameters such that the estimates of the Gaussian distribution parameters are restricted in a sub-space of reduced dimensionality. There are ...

We present a learning algorithm for hidden Markov models with continuous state and observation spaces. All necessary probability density functions are approximated using samples, along with density trees generated from such samples. A Monte Carlo version of Baum-Welch (EM) is employed to learn models from data, just as in regular HMM learning. Regularization during learning is obtained using an...

The discrimination technique for estimating parameters of Gaussian mixtures that is based on the Extended Baum-Welch transformations (EBW) has had significant impact on the speech recognition community. In this paper we introduce a general definition of a family of EBW transformations that can be associated with a weighted sum of updated and initial models. We compute a gradient steepness measu...

The scattered fields of a number of targets in free space are measured. Their complex natural resonances are extracted from the late time responses, using the generalized pencil-of-function method. The complex natural resonances, as the targets are immersed in a lossy medium, are investigated using Baum’s transform. The results of the complex natural resonances for various targets are expected ...

There is an increasing demand for systems which handle higher density, additional loads as seen in storage workload modelling, where workloads can be characterized on-line. This paper aims to find a workload model which processes incoming data and then updates its parameters "on-the-fly." Essentially, this will be an incremental hidden Markov model (IncHMM) with an improved Baum-Welch algorithm...