Treatment of rabbit skeletal muscle heavy meromyosin (HMM) with the sulfhydryl reagent N-ethylmaleimide (NEM) produces a species of HMM which remains tightly bound to actin in the presence of MgATP. NEM-HMM forms characteristic "arrowhead" complexes with actin which persist despite rinses with MgATP. NEM-HMM inhibits the actin activation of native HMM-ATPase activity, the superprecipitation of ...

This paper shows that the HMM whose state output vector includes static and dynamic feature parameters can be reformulated as a trajectory model by imposing the explicit relationship between the static and dynamic features. The derived model, named trajectory HMM, can alleviate the limitations of HMMs: i) constant statistics within an HMM state and ii) independence assumption of state output pr...

We present a comparison of 6 methods for classification of sports audio. For the feature extraction we have two choices: MPEG-7 audio features and Mel-scale Frequency Cepstrum Coefficients(MFCC). For the classification we also have two choices: Maximum Likelihood Hidden Markov Models(ML-HMM) and Entropic Prior HMM(EP-HMM). EP-HMM, in turn, have two variations: with and without trimming of the m...

The duration high-order hidden Markov model (DHO-HMM) can capture the dynamic evolution of a physical system more precisely than can the first-order hidden Markov model (HMM). The relations among the DHO-HMM, high-order HMM (HOHMM), hidden semi-Markov model (HSMM), and HMM are presented and discussed. Recursive forward and backward probability functions for the partial observation sequence were...

A novel formulation of the nonstationary-state hidden Markov model (NS-HMM), employed as the speech model and serving as the theoretical basis for the construction of a speech enhancement system, is presented in this paper. The NS-HMM is used as a compact, parametric model, generalized from the stationary-state HMM, for describing clean speech statistics in the construction of the minimum mean-...

National Cancer Institute, USPHS. Presented in part at the 62nd Annual Meeting of the American Association for Cancer Research, Inc., Chicago. 111..April 1971 (37). 2Career Development Awardee of the National Cancer Institute (IK4-CA-8245-05). 3The abbreviations used are: HMM, hexamethylmelamine [2,4,6tris(dimethylamino)-i-lriazine] (NSC-13875); TEM, triethylenemelamine [2,4,6-tris(l-aziridinyl...

today, there arevarious statistical models for the discrete simulation of the rainfall occurrence/non-occurrence with more emphasizing on long-term climatic statistics. nevertheless, the accuracy of such models or predictions should be improved in short timescale. in the present paper, it is assumed that the rainfall occurrence/non-occurrence sequences follow a two-layer hidden markov model (hm...

In this paper, we describe an application of the Forward-Backwards (F-B) algorithm for maximum likelihood training of hybrid HMM/Bayesian Network (BN) acoustic models. Previously, HMM/BN parameter estimation was based on a Viterbi training algorithm that requires two passes over the training data: one for BN learning and one for updating HMM transition probabilities. In this work, we first anal...

The most popular model used in automatic speech recognition is the hidden Markov model (HMM). Though good performance has been obtained with such models there are well known limitations in its ability to model speech. A variety of modifications to the standard HMM topology have been proposed to handle these problems. One approach is the factorial HMM. This paper introduces a new form of factori...

In this paper, a study of topology of Hidden Markov Model (HMM) used in speech recognition is addressed. Our main contribution is the introduction of the notion of trajectory folding phenomenon of HMM. In complex phonetic contexts and in speaker-variability, this phenomenon degrades the discriminability of HMM. The goal of this paper is to give some explanation and experimental evidence suggest...