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114
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.
Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models
, 1995
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Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chains
 IEEE Transactions on Speech and Audio Processing
, 1994
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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.
Learning Topological Maps with Weak Local Odometric Information
 IN PROCEEDINGS OF IJCAI97. IJCAI, INC
, 1997
"... Topological maps provide a useful abstraction for robotic navigation and planning. Although stochastic maps can theoretically be learned using the BaumWelch algorithm, without strong prior constraint on the structure of the model it is slow to converge, requires a great deal of data, and is o ..."
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Cited by 139 (4 self)
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Topological maps provide a useful abstraction for robotic navigation and planning. Although stochastic maps can theoretically be learned using the BaumWelch algorithm, without strong prior constraint on the structure of the model it is slow to converge, requires a great deal of data, and is often stuck in local minima. In this paper, we consider a special case of hidden Markov models for robotnavigation environments, in which states are associated with points in a metric configuration space. We assume that the robot has some odometric ability to measure relative transformations between its configurations. Such odometry is typically not precise enough to suffice for building a global map, but it does give valuable local information about relations between adjacent states. We present an extension of the BaumWelch algorithm that takes advantage of this local odometric information, yielding faster convergence to better solutions with less data.
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...
Continuous Speech Recognition Using Hidden Markov Models
 IEEE ASSP MAGAZINE
, 1990
"... Stochastic signal processing techniques have profoundly changed our perspective on speech processing. We have witnessed a progression from heuristic algorithms to detailed statistical approaches based on iterat ive analysis techniques. Markov modeling provides a mathematically rigorous approach t ..."
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Cited by 54 (9 self)
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Stochastic signal processing techniques have profoundly changed our perspective on speech processing. We have witnessed a progression from heuristic algorithms to detailed statistical approaches based on iterat ive analysis techniques. Markov modeling provides a mathematically rigorous approach to developing robust s tat is t ica l signal models. Since t h e i n t roduc t i on of Markov models t o speech processing in t h e middle 1970s. continuous speech recognition technology has come of age. Dramatic advances have been made in characterizing the temporal and spectral evolution of the speech signal. A t the same time, our appreciation o f t he need to explain complex acoustic manifestations b y integration of application constraints in to low level signal processing has grown. In th is paper, w e review the use of Markov models in continuous speech recognition. Markov models are presented as a generalization of i t s predecessor technology, Dynamic Programming. A unified view is offered in which bo th linguistic decoding and acoustic matching are integrated in to a single optimal network search framework.
An hmmbased approach for offline unconstrained handwritten word modeling and recognition
 IEEE Trans. Pattern Analysis and Machine Intelligence
, 1999
"... Abstract—This paper describes a hidden Markov modelbased approach designed to recognize offline unconstrained handwritten words for large vocabularies. After preprocessing, a word image is segmented into letters or pseudoletters and represented by two feature sequences of equal length, each consis ..."
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Cited by 44 (9 self)
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Abstract—This paper describes a hidden Markov modelbased approach designed to recognize offline unconstrained handwritten words for large vocabularies. After preprocessing, a word image is segmented into letters or pseudoletters and represented by two feature sequences of equal length, each consisting of an alternating sequence of shapesymbols and segmentationsymbols, which are both explicitly modeled. The word model is made up of the concatenation of appropriate letter models consisting of elementary HMMs and an HMMbased interpolation technique is used to optimally combine the two feature sets. Two rejection mechanisms are considered depending on whether or not the word image is guaranteed to belong to the lexicon. Experiments carried out on reallife data show that the proposed approach can be successfully used for handwritten word recognition. Index Terms—Handwriting modeling, preprocessing, segmentation, feature extraction, hidden Markov models, word recognition, rejection. æ 1
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 40 (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.