We describe the modelling of articulatory movements using (hidden) dynamical system models trained on Electro-Magnetic Articulograph (EMA) data. These models can be used for automatic speech recognition and to give insights into articulatory behaviour. They belong to a class of continuous-state Markov models, which we believe can offer improved performance over conventional Hidden Markov Models...

We present a learning algorithm for non-parametric hidden Markov models with continuous state and observation spaces. All necessary probability densities are approximated using samples, along with density trees generated from such samples. A Monte Carlo version of Baum-Welch (EM) is employed to learn models from data. Regularization during learning is achieved using an exponential shrinking tec...

We introduce a novel way to employ codebooks trained by Learning Vector Quantization together with hidden Markov models. In previous work, LVQ-codebooks have been used as frame labelers. The resulting label stream has been modeled and decoded by discrete observation HMMs. We present a way to extract more information out of the LVQ stage. This is accomplished by modeling the class-wise quantizat...

The subject of this paper is the integration of multi-layered Artificial Neural Networks (ANN) with probability density functions such as Gaussian mixtures found in continuous density Hidden Markov Models (HMM). In the first part of this paper we present an ANN/HMM hybrid in which all the parameters of the the system are simultaneously optimized with respect to a single criterion. In the second...

As a kind of statistical method, the technique of Hidden Markov Model (HMM) is widely used for speech recognition. In order to train the HMM to be more effective with much less amount of data, the Subspace Distribution Clustering Hidden Markov Model (SDCHMM), derived from the Continuous Density Hidden Markov Model (CDHMM), is introduced. With parameter tying, a new method to train SDCHMMs is de...

FARSI HANDWRITTEN WORD RECOGNITION USING CONTINUOUS HIDDEN MARKOV MODELS AND STRUCTURAL FEATURES

Hidden Markov models have proved to be a very exible class of models, with many and diverse applications. Recently Markov chain Monte Carlo (MCMC) techniques have provided powerful computational tools to make inferences about the parameters of hidden Markov models, and about the unobserved Markov chain, when the chain is deened in discrete time. We present a general algorithm, based on reversib...