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A tutorial on hidden Markov models and selected applications in speech recognition
 PROCEEDINGS OF THE IEEE
, 1989
"... Although initially introduced and studied in the late 1960s and early 1970s, statistical methods of Markov source or hidden Markov modeling have become increasingly popular in the last several years. There are two strong reasons why this has occurred. First the models are very rich in mathematical s ..."
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Cited by 5764 (1 self)
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Although initially introduced and studied in the late 1960s and early 1970s, statistical methods of Markov source or hidden Markov modeling have become increasingly popular in the last several years. There are two strong reasons why this has occurred. First the models are very rich in mathematical structure and hence can form the theoretical basis for use in a wide range of applications. Second the models, when applied properly, work very well in practice for several important applications. In this paper we attempt to carefully and methodically review the theoretical aspects of this type of statistical modeling and show how they have been applied to selected problems in machine recognition of speech.
From HMM's to Segment Models: A Unified View of Stochastic Modeling for Speech Recognition
, 1996
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Hidden Markov processes
 IEEE Trans. Inform. Theory
, 2002
"... Abstract—An overview of statistical and informationtheoretic aspects of hidden Markov processes (HMPs) is presented. An HMP is a discretetime finitestate homogeneous Markov chain observed through a discretetime memoryless invariant channel. In recent years, the work of Baum and Petrie on finite ..."
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Cited by 258 (5 self)
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Abstract—An overview of statistical and informationtheoretic aspects of hidden Markov processes (HMPs) is presented. An HMP is a discretetime finitestate homogeneous Markov chain observed through a discretetime memoryless invariant channel. In recent years, the work of Baum and Petrie on finitestate finitealphabet HMPs was expanded to HMPs with finite as well as continuous state spaces and a general alphabet. In particular, statistical properties and ergodic theorems for relative entropy densities of HMPs were developed. Consistency and asymptotic normality of the maximumlikelihood (ML) parameter estimator were proved under some mild conditions. Similar results were established for switching autoregressive processes. These processes generalize HMPs. New algorithms were developed for estimating the state, parameter, and order of an HMP, for universal coding and classification of HMPs, and for universal decoding of hidden Markov channels. These and other related topics are reviewed in this paper. Index Terms—Baum–Petrie algorithm, entropy ergodic theorems, finitestate channels, hidden Markov models, identifiability, Kalman filter, maximumlikelihood (ML) estimation, order estimation, recursive parameter estimation, switching autoregressive processes, Ziv inequality. I.
inference via Gibbs sampling of autoregressive time series subject to Markov mean and variance shifts
 Journal of Business and Economic Statistics
, 1993
"... We examine autoregressive time series models that are subject to regime switching. These shifts are determined by the outcome of an unobserved twostate indicator variable that follows a Markov process with unknown transition probabilities. A Bayesian framework is developed in which the unobserved s ..."
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Cited by 147 (5 self)
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We examine autoregressive time series models that are subject to regime switching. These shifts are determined by the outcome of an unobserved twostate indicator variable that follows a Markov process with unknown transition probabilities. A Bayesian framework is developed in which the unobserved states, one for each time point, are treated as missing data and then analyzed via the simulation tool of Gibbs sampling. This method is expedient because the conditional posterior distribution f the parameters, given the states, and the conditional posterior distribution of the states, given the parameters, all have a form amenable to Monte Carlo sampling. The approach is straightforward and generates marginal posterior distributions for all parameters of interest. Posterior distributions of the states, future observations, and the residuals, averaged over the parameter space are also obtained. Several examples with real and artificial data sets and weak prior information illustrate the usefulness of the methodology.
Speech Recognition in Noisy Environments
 Ph. D. Dissertation, ECE Department, CMU
, 1996
"... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Chapter 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 1.1. Thesis goals . . . . . . . . . . . . . . . . . . . . . ..."
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Cited by 115 (3 self)
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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Chapter 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 1.1. Thesis goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 1.2. Dissertation Outline . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Chapter 2 The SPHINXII Recognition System . . . . . . . . . . . . . . . . . . . . . . 17 2.1. An Overview of the SPHINXII System . . . . . . . . . . . . . . . . . . 17 2.1.1. Signal Processing . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.1.2. Hidden Markov Models . . . . . . . . . . . . . . . . . . . . . . 20 2.1.3. Recognition Unit . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.1.4. Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.1.5. Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 2.2. Experimental Tasks and Corpora . ...
Detecting Activities
 Journal of Visual Communication and Image Representation
, 1993
"... The recognition of repetitive movements characteristic of walking people, galloping horses, or flying birds is a routine function of the human visual system. It has been demonstrated that humans can recognize such activity solely on the basis of motion information. We present a novel computational a ..."
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Cited by 111 (5 self)
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The recognition of repetitive movements characteristic of walking people, galloping horses, or flying birds is a routine function of the human visual system. It has been demonstrated that humans can recognize such activity solely on the basis of motion information. We present a novel computational approach for detecting such activities in real image sequences on the basis of the periodic nature of their signatures. The approach suggests a lowlevel feature based activity recognition mechanism. This contrasts with earlier modelbased approaches for recognizing such activities. 1 Introduction The motion recognition ability of the human visual system is remarkable. People are able to distinguish both highly structured motion, such as that produced by walking, running, swimming or flying birds, and more statistical patterns such as that due to blowing snow, flowing water or fluttering leaves. More subtle movement characteristics can be distinguished as well. For example, human observers ...
Detection and Recognition of Periodic, Nonrigid Motion
 INTERNATIONAL JOURNAL OF COMPUTER VISION
, 1997
"... The recognition of nonrigid motion, particularly that arising from human movement (and by extension from the locomotory activity of animals) has typically made use of highlevel parametric models representing the various body parts (legs, arms, trunk, head etc.) and their connections to each other. ..."
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Cited by 97 (0 self)
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The recognition of nonrigid motion, particularly that arising from human movement (and by extension from the locomotory activity of animals) has typically made use of highlevel parametric models representing the various body parts (legs, arms, trunk, head etc.) and their connections to each other. Such modelbased recognition has been successful in some cases; however, the methods are often difficult to apply to realworld scenes, and are severely limited in their generalizability. The first problem arises from the difficulty of acquiring and tracking the requisite model parts, usually specific joints such as knees, elbows or ankles. This generally requires some prior highlevel understanding and segmentation of the scene, or initialization by a human operator. The second problem, with generalization, is due to the fact that the human model is not much good for dogs or birds, and for each new type of motion, a new model must be handcrafted. In this paper, we show that the recognition...
Connectionist Probability Estimation in HMM Speech Recognition
 IEEE Transactions on Speech and Audio Processing
, 1992
"... This report is concerned with integrating connectionist networks into a hidden Markov model (HMM) speech recognition system, This is achieved through a statistical understanding of connectionist networks as probability estimators, first elucidated by Herve Bourlard. We review the basis of HMM speech ..."
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Cited by 92 (24 self)
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This report is concerned with integrating connectionist networks into a hidden Markov model (HMM) speech recognition system, This is achieved through a statistical understanding of connectionist networks as probability estimators, first elucidated by Herve Bourlard. We review the basis of HMM speech recognition, and point out the possible benefits of incorporating connectionist networks. We discuss some issues necessary to the construction of a connectionist HMM recognition system, and describe the performance of such a system, including evaluations on the DARPA database, in collaboration with Mike Cohen and Horacio Franco of SRI International. In conclusion, we show that a connectionist component improves a state of the art HMM system. ii Part I INTRODUCTION Over the past few years, connectionist models have been widely proposed as a potentially powerful approach to speech recognition (e.g. Makino et al. (1983), Huang et al. (1988) and Waibel et al. (1989)). However, whilst connec...