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22
Gene networks inference using dynamic Bayesian networks
 Bioinformatics
, 2003
"... This article deals with the identification of gene regulatory networks from experimental data using a statistical machine learning approach. A stochastic model of gene interactions capable of handling missing variables is proposed. It can be described as a dynamic Bayesian network particularly wel ..."
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Cited by 64 (0 self)
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This article deals with the identification of gene regulatory networks from experimental data using a statistical machine learning approach. A stochastic model of gene interactions capable of handling missing variables is proposed. It can be described as a dynamic Bayesian network particularly well suited to tackle the stochastic nature of gene regulation and gene expression measurement. Parameters of the model are learned through a penalized likelihood maximization implemented through an extended version of EM algorithm. Our approach is tested against experimental data relative to the S.O.S. DNA Repair network of the Escherichia coli bacterium. It appears to be able to extract the main regulations between the genes involved in this network. An added missing variable is found to model the main protein of the network. Good prediction abilities on unlearned data are observed. These first results are very promising: they show the power of the learning algorithm and the ability of the model to capture gene interactions.
Dynamical modeling with kernels for nonlinear time series prediction
"... We consider the question of predicting nonlinear time series. Kernel Dynamical Modeling, a new method based on kernels, is proposed as an extension to linear dynamical models. The kernel trick is used twice: first, to learn the parameter of the model, and second, to compute preimages of the time ser ..."
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Cited by 18 (1 self)
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We consider the question of predicting nonlinear time series. Kernel Dynamical Modeling, a new method based on kernels, is proposed as an extension to linear dynamical models. The kernel trick is used twice: first, to learn the parameter of the model, and second, to compute preimages of the time series predicted in the feature space by means of Support Vector Regression. Our model shows strong connection with the classic Kalman Filter model, with the kernel feature space as hidden state space. Kernel Dynamical Modeling is tested against two benchmark time series and achieves high quality predictions. 1
Factor analysed hidden Markov models for Speech Recognition
 COMPUTER SPEECH AND LANGUAGE
, 2004
"... Recently various techniques to improve the correlation model of feature vector elements in speech recognition systems have been proposed. Such techniques include semitied covariance HMMs and systems based on factor analysis. All these schemes have been shown to improve the speech recognition perfor ..."
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Cited by 17 (6 self)
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Recently various techniques to improve the correlation model of feature vector elements in speech recognition systems have been proposed. Such techniques include semitied covariance HMMs and systems based on factor analysis. All these schemes have been shown to improve the speech recognition performance without dramatically increasing the number of model parameters compared to standard diagonal covariance Gaussian mixture HMMs. This paper introduces a general form of acoustic model, the factor analysed HMM. A variety of configurations of this model and parameter sharing schemes, some of which correspond to standard systems, were examined. An EM algorithm for the parameter optimisation is presented along with a number of methods to increase the e#ciency of training. The performance of FAHMMs on medium to large vocabulary continuous speech recognition tasks was investigated. The experiments show that without elaborate complexity control an equivalent or better performance compared to a standard diagonal covariance Gaussian mixture HMM system can be achieved with considerably fewer parameters.
Linear Gaussian models for speech recognition
 CAMBRIDGE UNIVERSITY
, 2004
"... Currently the most popular acoustic model for speech recognition is the hidden Markov model (HMM). However, HMMs are based on a series of assumptions some of which are known to be poor. In particular, the assumption that successive speech frames are conditionally independent given the discrete stat ..."
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Cited by 16 (0 self)
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Currently the most popular acoustic model for speech recognition is the hidden Markov model (HMM). However, HMMs are based on a series of assumptions some of which are known to be poor. In particular, the assumption that successive speech frames are conditionally independent given the discrete state that generated them is not a good assumption for speech recognition. State space models may be used to address some shortcomings of this assumption. State space models are based on a continuous state vector evolving through time according to a state evo
Switching Linear Dynamical Systems For Speech Recognition
, 2003
"... This paper describes the application of RaoBlackwellised Gibbs sampling (RBGS) to speech recognition using switching linear dynamical systems (SLDSs) as the acoustic model. ..."
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Cited by 15 (6 self)
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This paper describes the application of RaoBlackwellised Gibbs sampling (RBGS) to speech recognition using switching linear dynamical systems (SLDSs) as the acoustic model.
Product of Gaussians for speech recognition
 Computer Speech & Language
, 2003
"... 1 Introduction Mixture of Gaussians (MoG) are commonly used as the state representation in hidden Markov model (HMM) based speech recognition. These Gaussian mixture models are easy to train using expectation maximisation (EM) techniques [4] and are able to approximate any distribution given a suffi ..."
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Cited by 11 (2 self)
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1 Introduction Mixture of Gaussians (MoG) are commonly used as the state representation in hidden Markov model (HMM) based speech recognition. These Gaussian mixture models are easy to train using expectation maximisation (EM) techniques [4] and are able to approximate any distribution given a sufficient number of components. However, only a limited number of parameters can be effectively trained given a finite quantity of training data. This limitation restricts the ability of MoG systems to model highly complex distributions. A range of distributed representations have been developed to overcome this problem. These distributed representations may be split into two basic forms. The first assumes that the sources are asynchronous. The second assumes that the sources are synchronous. o o ot1 t t+1 q q qt1 t t+1
Implicit Pronunciation Modelling in ASR
, 2002
"... Modelling of pronunciation variability is an important part of the acoustic model of a speech recognition system. Good pronunciation models contribute to the robustness and portability of a speech recogniser. Usually pronunciation modelling is associated with the recognition lexicon which allows a d ..."
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Cited by 7 (2 self)
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Modelling of pronunciation variability is an important part of the acoustic model of a speech recognition system. Good pronunciation models contribute to the robustness and portability of a speech recogniser. Usually pronunciation modelling is associated with the recognition lexicon which allows a direct control of HMM selection. However, in stateoftheart systems the use of clustering techniques has considerable crosseffects for the dictionary design. Most large vocabulary speech recognition systems make use of a dictionary with multiple possible pronunciation variants per word. In this paper a method for a consistent reduction of the number of pronunciation variants to one pronunciation per word is described. Using the single pronunciation dictionaries similar or better word error rate performance is achieved both on Wall Street Journal and Switchboard data.
Nonlinear Time Series Filtering, Smoothing and Learning using the Kernel Kalman Filter
"... In this paper, we propose a new model, the Kernel Kalman Filter, to perform various nonlinear time series processing. This model is based on the use of Mercer kernel functions in the framework of the Kalman Filter or Linear Dynamical Systems. Thanks to the kernel trick, all the equations involved in ..."
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Cited by 5 (0 self)
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In this paper, we propose a new model, the Kernel Kalman Filter, to perform various nonlinear time series processing. This model is based on the use of Mercer kernel functions in the framework of the Kalman Filter or Linear Dynamical Systems. Thanks to the kernel trick, all the equations involved in our model to perform filtering, smoothing and learning tasks, only require matrix algebra calculus whilst providing the ability to model complex time series. In particular, it is possible to learn dynamics from some nonlinear noisy time series implementing an exact EM procedure. When predictions in the original input space are needed, an efficient and original preimage learning strategy is proposed. 1.
Transformation Streams and the HMM Error Model
 Computer Speech and Language
, 2001
"... The most popular model used in automatic speech recognition is the hidden Markov model (HMM). Though good performance has been obtained with such models there are well known limitations for its ability to model speech. A variety of modications to the standard HMM topology have been proposed to handl ..."
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Cited by 1 (1 self)
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The most popular model used in automatic speech recognition is the hidden Markov model (HMM). Though good performance has been obtained with such models there are well known limitations for its ability to model speech. A variety of modications to the standard HMM topology have been proposed to handle these problems. One such scheme is the factorial HMM. This paper introduces a new form of factorial HMM which makes use of transformation streams. This new scheme is a generalisation of the standard factorial HMM and other related schemes in speech processing. A particular form of this model, the HMM error model (HEM) is described in detail. The HEM is evaluated on two standard large vocabulary speaker independent speech recognition tasks. On both tasks signicant reductions in word error rate are obtained over standard HMMbased systems. 2 1