In state-of-art large vocabulary continuous speech recognition (LVCSR) systems, HMM state-tying is often used to achieve good balance between the model resolution and robustness. In this paradigm, tied HMM states share a single set of parameters and are nondistinguishable. To capture the fine differences among tied HMM states, a probabilistic classification of HMM states (PCHMM) is proposed in ...

In the present paper, a trajectory model, derived from the hidden Markov model (HMM) by imposing explicit relationships between static and dynamic feature vector sequences, is developed and evaluated. The derived model, named trajectory HMM, can alleviate some limitations of the standard HMM, which are i) piece-wise constant statistics within a state and ii) conditional independence assumption ...

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-...