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From HMM's to Segment Models: A Unified View of Stochastic Modeling for Speech Recognition
, 1996
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A new approach to analyzing gene expression time series data
, 2002
"... 1 Introduction Principled methods for estimating unobserved timepoints,clustering, and aligning microarray gene expression timeseries are needed to make such data useful for detailed analysis. Datasets measuring temporal behavior of thousands of genes offer rich opportunities for computational bio ..."
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Cited by 84 (3 self)
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1 Introduction Principled methods for estimating unobserved timepoints,clustering, and aligning microarray gene expression timeseries are needed to make such data useful for detailed analysis. Datasets measuring temporal behavior of thousands of genes offer rich opportunities for computational biologists. For example, Dynamic Bayesian Networks may be usedto build models and try to understand how genetic responses unfold. However, such modeling frameworks need a sufficient quantity of data in the appropriate format. Current gene expression timeseries data often do not meet these requirements, since they may be missing data points, sampled nonuniformly, and measure biological processes that exhibittemporal variation.
I.: Continuous representations of timeseries gene expression data
 J Comput Biol
"... We present algorithms for timeseries gene expression analysis that permit the principled estimation of unobserved time points, clustering, and dataset alignment. Each expression pro � le is modeled as a cubic spline (piecewise polynomial) that is estimated from the observed data and every time poin ..."
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Cited by 80 (10 self)
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We present algorithms for timeseries gene expression analysis that permit the principled estimation of unobserved time points, clustering, and dataset alignment. Each expression pro � le is modeled as a cubic spline (piecewise polynomial) that is estimated from the observed data and every time point in � uences the overall smooth expression curve. We constrain the spline coef � cients of genes in the same class to have similar expression patterns, while also allowing for gene speci � c parameters. We show that unobserved time points can be reconstructed using our method with 10–15 % less error when compared to previous best methods. Our clustering algorithm operates directly on the continuous representations of gene expression pro � les, and we demonstrate that this is particularly effective when applied to nonuniformly sampled data. Our continuous alignment algorithm also avoids dif � culties encountered by discrete approaches. In particular, our method allows for control of the number of degrees of freedom of the warp through the speci � cation of parameterized functions, which helps to avoid over � tting. We demonstrate that our algorithm produces stable lowerror alignments on real expression data and further show a speci � c application to yeast knockout data that produces biologically meaningful results. Key words: time series expression data, missing value estimation, clustering, alignment. 1.
Graphical models and automatic speech recognition
 Mathematical Foundations of Speech and Language Processing
, 2003
"... Graphical models provide a promising paradigm to study both existing and novel techniques for automatic speech recognition. This paper first provides a brief overview of graphical models and their uses as statistical models. It is then shown that the statistical assumptions behind many pattern recog ..."
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Cited by 72 (13 self)
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Graphical models provide a promising paradigm to study both existing and novel techniques for automatic speech recognition. This paper first provides a brief overview of graphical models and their uses as statistical models. It is then shown that the statistical assumptions behind many pattern recognition techniques commonly used as part of a speech recognition system can be described by a graph – this includes Gaussian distributions, mixture models, decision trees, factor analysis, principle component analysis, linear discriminant analysis, and hidden Markov models. Moreover, this paper shows that many advanced models for speech recognition and language processing can also be simply described by a graph, including many at the acoustic, pronunciation, and languagemodeling levels. A number of speech recognition techniques born directly out of the graphicalmodels paradigm are also surveyed. Additionally, this paper includes a novel graphical analysis regarding why derivative (or delta) features improve hidden Markov modelbased speech recognition by improving structural discriminability. It also includes an example where a graph can be used to represent language model smoothing constraints. As will be seen, the space of models describable by a graph is quite large. A thorough exploration of this space should yield techniques that ultimately will supersede the hidden Markov model.
What HMMs can do
, 2002
"... Since their inception over thirty years ago, hidden Markov models (HMMs) have have become the predominant methodology for automatic speech recognition (ASR) systems — today, most stateoftheart speech systems are HMMbased. There have been a number of ways to explain HMMs and to list their capabil ..."
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Cited by 45 (5 self)
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Since their inception over thirty years ago, hidden Markov models (HMMs) have have become the predominant methodology for automatic speech recognition (ASR) systems — today, most stateoftheart speech systems are HMMbased. There have been a number of ways to explain HMMs and to list their capabilities, each of these ways having both advantages and disadvantages. In an effort to better understand what HMMs can do, this tutorial analyzes HMMs by exploring a novel way in which an HMM can be defined, namely in terms of random variables and conditional independence assumptions. We prefer this definition as it allows us to reason more throughly about the capabilities of HMMs. In particular, it is possible to deduce that there are, in theory at least, no theoretical limitations to the class of probability distributions representable by HMMs. This paper concludes that, in search of a model to supersede the HMM for ASR, we should rather than trying to correct for HMM limitations in the general case, new models should be found based on their potential for better parsimony, computational requirements, and noise insensitivity.
Production models as a structural basis for automatic speech recognition
 Speech Communication
, 1997
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Structured speech modeling
 IEEE Transactions on Audio, Speech and Language Processing (Special Issue on Rich Transcription
, 2006
"... Abstract—Modeling dynamic structure of speech is a novel paradigm in speech recognition research within the generative modeling framework, and it offers a potential to overcome limitations of the current hidden Markov modeling approach. Analogous to structured language models where syntactic structu ..."
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Cited by 37 (20 self)
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Abstract—Modeling dynamic structure of speech is a novel paradigm in speech recognition research within the generative modeling framework, and it offers a potential to overcome limitations of the current hidden Markov modeling approach. Analogous to structured language models where syntactic structure is exploited to represent longdistance relationships among words [5], the structured speech model described in this paper makes use of the dynamic structure in the hidden vocal tract resonance space to characterize longspan contextual influence among phonetic units. A general overview is provided first on hierarchically classified types of dynamic speech models in the literature. A detailed account is then given for a specific model type called the hidden trajectory model, and we describe detailed steps of model construction and the parameter estimation algorithms. We show how the use of resonance target parameters and their temporal filtering enables joint modeling of longspan coarticulation and phonetic reduction effects. Experiments on phonetic recognition evaluation demonstrate superior recognizer performance over a modern hidden Markov modelbased system. Error analysis shows that the greatest performance gain occurs within the sonorant speech class. Index Terms—Hidden dynamics, hidden trajectory, long span modeling, maximumlikelihood, nonlinear prediction, parameter learning, structured modeling, vocal tract resonance. I.
Probabilistictrajectory Segmental HMMs. Computer Speech and Language
, 1999
"... “Segmental hidden Markov models ” (SHMMs) are intended to overcome important speechmodelling limitations of the conventionalHMM approach by representing sequences (or segments) of features and incorporating the concept of trajectories to describe how features change over time. A novel feature of t ..."
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Cited by 32 (2 self)
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“Segmental hidden Markov models ” (SHMMs) are intended to overcome important speechmodelling limitations of the conventionalHMM approach by representing sequences (or segments) of features and incorporating the concept of trajectories to describe how features change over time. A novel feature of the approach presented in this paper is that extrasegmental variability between different examples of a subphonemic speech segment is modelled separately from intrasegmental variability within any one example. The extrasegmental component of the model is represented in terms of variability in the trajectory parameters, and these models are therefore referred to as “probabilistictrajectory segmental HMMs ” (PTSHMMs). This paper presents the theory of PTSHMMs using a linear trajectory description characterized by slope and midpoint parameters, and presents theoretical and experimental comparisons between different types of PTSHMMs, simpler SHMMs and conventional HMMs. Experiments have demonstrated that, for any given feature set, a linear PTSHMM can substantially reduce the error rate in comparison with a conventional HMM, both for a connecteddigit recognition task and for a phonetic classification task. Performance benefits have been demonstrated from incorporating a linear trajectory description and additionally from modelling variability in the midpoint parameter. c ○ 1999 British Crown Copyright/DERA 1.
Speech trajectory discrimination using the minimum classification error learning
 IEEE Trans. Speech and Audio Processing, Vol.6
, 1998
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