In this paper, we extend the Maximum Likelihood (ML) training algorithm to the Minimum classificnrtion Emor (MCE) training algorithm for optimal estimation of the state-dependent polynomial coefficients in the trended HMM [2]. The problem of automatic speech recognition is viewed as a discriminative dynamic data-fitting problem, where nlotiue (not absolute) closeness in fitting an array of dyna...

Thehigh surface/volumeratio andmechanical stabilityunder cyclic strainmakespolycrystallineNieMneGa metallic foams attractive for magnetic refrigeration. By means of comparison with a polycrystalline bulk material, we have demonstrated that the porous structure of Ni54.8Mn20.2Ga25.0 open-cells metallic foams (porosity varying between44% and58%) reduces the temperature spanof the phase transition...

We discuss some inference problems associated with the fractional Ornstein-Uhlenbeck (fO-U) process driven by the fractional Brownian motion (fBm). In particular, we are concerned with the estimation of the drift parameter, assuming that the Hurst parameter H is known and is in [1/2, 1). Under this setting we compute the distributions of the maximum likelihood estimator (MLE) and the minimum co...

In this paper, we extend the Maximum Likelihood (ML) training algorithm to the Minimum Classiica-tion Error (MCE) training algorithm for discriminatively estimating the state-dependent polynomial coeecients in the stochastic trajectory model or the trended HMM originally proposed in 2]. The main motivation of this extension is the new model space for smoothness-constrained, state-bound speech t...

A novel method to improve the generalization performance of the Minimum Classification Error (MCE) / Generalized Probabilistic Descent (GPD) learning is proposed. The MCE/GPD learning proposed by Juang and Katagiri in 1992 results in better recognition performance than the maximum-likelihood (ML) based learning in various areas of pattern recognition. Despite its superiority in recognition perf...

In this paper, a concatenated "super" string model based minimum classification error (MCE) model adaptation approach is described. We show that the error rate minimization in the proposed approach can be formulated into maximizing a special ratio of two positive functions. The proposed string model is used to derive the growth transform based error rate minimization for MCE linear regression (...

In this paper, a formally unifying approach for a class of discriminative training criteria including Maximum Mutual Information (MMI) and Minimum Classification Error (MCE) criterion is presented, including the optimization methods gradient descent (GD) and extended Baum-Welch (EB) algorithm. Comparisons are discussed for the MMI and the MCE criterion, including the determination of the sets o...

The seismic hazard analysis is concerned with getting an estimate of the strong-motion parameters at a site for the purpose of earthquake resistant design or seismic safety assessment. For generalized applications, seismic hazard analysis can also be used to prepare macro or micro zoning maps of an area by estimating the strong-motion parameters for a closely spaced grid of sites. Two basic met...

In order to obtain “table-like” magnetocaloric effect (MCE), multiple-phase Gd56Ni15Al27Zr2 alloy was prepared by arc-melting followed by suck-casting method. Powder x-ray diffraction and calorimetric measurements reveal that the sample contains both glassy and crystalline phases. The fraction of the glassy phase is about 62%, estimated from the heat enthalpy of the crystallization. The crystal...