Factorial Hidden Markov Models
Machine Learning - Special issue on learning with probabilistic representations
A unifying review of linear Gaussian models
Neural Computation
Mixtures of probabilistic principal component analyzers
Neural Computation
Speech recognition with dynamic bayesian networks
Speech recognition with dynamic bayesian networks
Online kernel density estimation for interactive learning
Image and Vision Computing
ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
Audio query by example using similarity measures between probability density functions of features
EURASIP Journal on Audio, Speech, and Music Processing - Special issue on scalable audio-content analysis
A hierarchical feature fusion framework for adaptive visual tracking
Image and Vision Computing
SAMT'10 Proceedings of the 5th international conference on Semantic and digital media technologies
Language model cross adaptation for LVCSR system combination
Computer Speech and Language
Collaborative topic regression with social regularization for tag recommendation
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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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 (PoG). Here, each expert is a Gaussian mixture model. In this work, the PoG model is presented within a hidden Markov model framework. This allows the classification of variable length data, such as speech data. Training and initialisation procedures are described for this PoG system. The relationship of the PoG system with other schemes, including covariance modeling schemes, is also discussed. In addition the scheme is shown to be related to a standard speech recognition approach, multiple stream systems. The PoG system performance is examined on an automatic speech recognition task, Switchboard. The performance is compared to standard Gaussian mixture systems and multiple stream systems.