The main goal in this research is to find out possible ways to built hybrid systems, based on neural network (NN) and hidden Markov (HMM) models, for the task of automatic speech recognition. The investigation that has been conducted covers different types of neural network and hidden Markov models, and the combination of them into some hybrid models. The neural networks used were basically MLP...

This paper presents a novel method for modeling phonetic context using linear context transforms. Initial investigations have shown the feasibility of synthesising context dependent models from context independent models through weighted interpolation of the peripheral states of a given hidden markov model with its adjacent model. This idea can be further extended, to maximum likelihood estimat...

Statistical speech recognition using continuous-density hidden Markov models (CDHMMs) has yielded many practical applications. However, in general, mismatches between the training data and input data significantly degrade recognition accuracy. Various acoustic model adaptation techniques using a few input utterances have been employed to overcome this problem. In this article, we survey these a...

Today, most of the state-of-the-art speech recognizers are based on Hidden Markov modeling. Using semi-continuous or continuous density Hidden Markov Models, the computation of emission probabilities requires the evaluation of mixture Gaussian probability density functions. Since it is very expensive to evaluate all the Gaussians of the mixture density codebook, many recognizers only compute th...

Recognition of human behaviours requires modeling the underlying spatial and temporal structures of their motion patterns. Such structures are intrinsically probabilistic and therefore should be modelled as stochastic processes. In this paper we introduce a framework to recognise behaviours based on both learning prior and continuous propagation of density models of behaviour patterns. Prior is...

We introduce the minimal maximally predictive models ( -machines) of processes generated by certain hidden semi-Markov models. Their causal states are either hybrid discrete-continuous or continuous random variables and causal-state transitions are described by partial differential equations. Closed-form expressions are given for statistical complexities, excess entropies, and differential info...