Hidden Markov Models are commonly trained using algorithms derived from gradient-based methods such as the Baum-Welch procedure. We describe a new representation of discrete observation HMMs that permits them to be trained using Population-Based Incremental Learning (PBIL), a variant of genetic learning that combines evolutionary optimization and hill-climbing Baluja and Caruana, 1995]. In this...

We present a new algorithm to estimate the parameters of a Hidden Markov Model (HMM), specifically the transition probability matrix of the hidden states and the emission probabilities, given an observed sequence of data. The algorithm uses the number of transitions present in the observed label sequence and computes the parameters in an iterative fashion. We present experiments that demonstrat...

The EM algorithm, e.g. as Baum-Welch reestimation, is an important tool for parameter estimation in discrete-time Hidden Markov Models. We present a direct reestimation of rate constants for applications in which the underlying Markov process is continuous in time. Previous estimation of discrete-time transition probabilities is not necessary.

The EM algorithm, e.g., the Baum–Welch re-estimation, is an important tool for parameter estimation in discrete-time hidden Markov models. We present a direct re-estimation of rate constants for applications in which the underlying Markov process is continuous in time. Previous estimation of discrete-time transition probabilities is not necessary.

3 Baum-Welch Algorithm (EM) 5 3.1 Outline of Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3.2 First Term of Lc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3.3 Second Term of Lc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 3.4 Third Term of Lc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 3.4.1 Pois...

Gibbs distribution is used to represent fuzzy codebooks of individual speakers. The method of fuzzy evolutionary programming is employed to create the fuzzy codebooks and also to train hidden Markov models of speakers. This method increases the chance of attaining global maxima in Baum-Welch algorithm for hidden Markov model re-estimation. The experiments show the results of speaker identificat...

Hidden Markov chain, or field, models, with observations in a Euclidean space, play major role across signal and image processing. The present work provides statistical framework which can be used to extend these along related, popular algorithms (such as the Baum-Welch algorithm), case where lie Riemannian manifold. It is motivated by potential use of hidden chains fields, manifolds, models fo...

We study an equivariant co-assembly map that is dual to the usual Baum–Connes assembly map and closely related to coarse geometry, equivariant Kasparov theory, and the existence of dual Dirac morphisms. As applications, we prove the existence of dual Dirac morphisms for groups with suitable compactifications, that is, satisfying the Carlsson–Pedersen condition, and we study a K–theoretic counte...

The EC4 European syllabus for post-graduate training in clinical chemistry and laboratory medicine: Version 4 2012. Wieringa G, Zerah S, Jansen R, Simundic AM, Queralto J, Solnica B, Gruson D, Tomberg K, Riittinen L, Baum H, Brochet JP, Buhagiar G, Charilaou C, Grigore C, Johnsen AH, Kappelmayer J, Majkic-Singh N, Nubile G, O’ Mullane J, Opp M, Pupure S, Racek J, Reguengo H, Rizos D, Rogic D, S...

This paper tests speech recognition using prosody dependent allophone models. The log likehoods of various prosodically labeled phonemes are calculated using Baum-Welsh re-estimation. These log likehoods are then compared to log likehoods of non-prosodically labeled phonemes. Based on the comparison of these log likehoods, it can be concluded that modeling all prosodic information directly in t...