An approach for person identification is described based on spatio-temporal analysis of the talking face. A person is represented by a parametric model of the visible speech articulators and their temporal characteristics during speech production. The model consists of shape parameters, representing the lip contour and intensity parameters representing the grey level distribution in the mouth r...

This paper proposes a statistical-structural character learning algorithm based on hidden Markov model for handwritten Nushu character recognition. The stroke relationships of a Nushu character reflect its structure, which can be statistically represented by the hidden markov model. Based on the prior knowledge of character structures, we design an adaptive statisticalstructural character learn...

The Double Chain Markov Model is a fully Markovian model for the representation of time-series in random environment. In this article, we s h o w that it can handle transitions of high-order between both a set of obsevations and a set of hidden states. In order to reduce the number of parameters, each transition matrix can be replaced by a Mixture Transition Model. We provide a complete derivat...

Real-time estimation of crop progress stages is critical to the US agricultural economy and decision making. In this paper, a Hidden Markov Model (HMM) based method combining multisource features has been presented. The multisource features include mean Normalized Difference Vegetation Index (NDVI), fractal dimension, and Accumulated Growing Degree Days (AGDDs). In our case, these features are ...

This paper presents a novel solution to the problem of isolated digit recognition in background music. A Factorial Hidden Markov Model (FHMM) architecture is proposed to accurately model the simultaneous occurrence of two independent processes, such as an utterance of a digit and an excerpt of music. The FHMM is implemented with its equivalent HMM by extending Nadas’ MIXMAX algorithm to a mixtu...

In this paper, we develop a parameter estimation method for factorially parametrized models such as Factorial Gaussian Mixture Model and Factorial Hidden Markov Model. Our contributions are two-fold. First, we show that the emission matrix of the standard Factorial Model is unidentifiable even if the true assignment matrix is known. Secondly, we address the issue of identifiability by making a ...

Recently there has been interest in the use of classifiers based on the product of experts (PoE) framework. PoEs offer an alternative to the standard mixture of experts (MoE) framework. It may be viewed as examining the intersection of a series of experts, rather than the union as in the MoE framework. This paper presents a particular implementation of PoEs, the normalised product of Gaussians ...

Recent studies suggest that a hybrid speech recognition system based on a hidden Markov model (HMM) with a neural network (NN) subsystem as the estimator of the state conditional observation probability may have some advantages over the conventional HMMs with Gaussian mixture models for the observation probabilities. The HMM and NN modules are typically treated as separate entities in a hybrid ...

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...

In continuous speech recognition featuring hidden Markov model (HMM), word N-gram and time-synchronous beam search, a local modeling mismatch in the HMM will often cause the recognition performance to degrade. To cope with this problem, this paper proposes a method of restructuring Gaussian mixture pdfs in a pre-trained speaker-independent HMM based on speech data. In this method, mixture compo...