Results 1  10
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22
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 4597 (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.
Global Optimization of a Neural Network  Hidden Markov Model Hybrid
 IEEE Transactions on Neural Networks
, 1991
"... In this paper an original method for integrating Artificial Neural Networks (ANN) with Hidden Markov Models (HMM) is proposed. ANNs are suitable to perform phonetic classification, whereas HMMs have been proven successful at modeling the temporal structure of the speech signal. In the approach descr ..."
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Cited by 70 (16 self)
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In this paper an original method for integrating Artificial Neural Networks (ANN) with Hidden Markov Models (HMM) is proposed. ANNs are suitable to perform phonetic classification, whereas HMMs have been proven successful at modeling the temporal structure of the speech signal. In the approach described here, the ANN outputs constitute the sequence of observation vectors for the HMM. An algorithm is proposed for global optimization of all the parameters. Results on speakerindependent recognition experiments using this integrated ANNHMM system on the TIMIT continuous speech database are reported. 1 Introduction In spite of the fact that speech exhibits features that cannot be represented by a firstorder Markov model, Hidden Markov Models (HMMs) of speech units (e.g., phonemes) have been used with a good degree of success in Automatic Speech Recognition (ASR) (Rabiner & Levinson 85; Lee & Hon 89). Artificial Neural Networks (ANNs) have proven to be useful for classifying speech prop...
Training hidden markov models with multiple observationsa combinatorial method
 IEEE Trans. Pattern Anal. Mach. Intell
"... Hidden Markov models (HMMs) are stochastic models capable of statistical learning and classification. They have been applied in speech recognition and handwriting recognition because of their great adaptability and versatility in handling sequential signals. On the other hand, as these models have a ..."
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Cited by 18 (0 self)
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Hidden Markov models (HMMs) are stochastic models capable of statistical learning and classification. They have been applied in speech recognition and handwriting recognition because of their great adaptability and versatility in handling sequential signals. On the other hand, as these models have a complex structure, and also because the involved data sets usually contain uncertainty, it is difficult to analyze the multiple observation training problem without certain assumptions. For many years researchers have used Levinson’s training equations in speech and handwriting applications simply assuming that all observations are independent of each other. This paper present a formal treatment of HMM multiple observation training without imposing the above assumption. In this treatment, the multiple observation probability is expressed as a combination of individual observation probabilities without losing generality. This combinatorial method gives one more freedom in making different dependenceindependence assumptions. By generalizing Baum’s auxiliary function into this framework and building up an associated objective function using Lagrange multiplier method, it is proved that the derived training equations guarantee the maximization of the objective function. Furthermore, we show that Levinson’s training equations can be easily derived as a special case in this treatment.
A SyntaxDirected Level Building Algorithm for Large Vocabulary Handwritten Word Recognition
 IN PROC. 4TH INTERNATIONAL WORKSHOP ON DOCUMENT ANALYSIS SYSTEMS
, 2000
"... This paper describes a large vocabulary handwritten word recognition system based on a syntaxdirected level building algorithm (SDLBA) that incorporates contextual information. The sequences of observations extracted from the input images are matched against the entries of a treestructure lexi ..."
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Cited by 6 (4 self)
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This paper describes a large vocabulary handwritten word recognition system based on a syntaxdirected level building algorithm (SDLBA) that incorporates contextual information. The sequences of observations extracted from the input images are matched against the entries of a treestructure lexicon where each node is represented bya 10state character HMM. The search proceeds breadthfirst and each node is decoded by the SDLBA. Contextual information about writing styles and case transitions is injected between the levels of the SDLBA. An
The recognition of handwritten digit strings of unknown length using hidden Markov models
 In Proc. of 14 th International Conference Pattern Recognition (ICPR
, 1998
"... We apply an HMMbased text recognition system to the recognition of handwritten digit strings of unknown length. The algorithm is tailored to the input data by controlling the maximum number of levels searched by the Level Building (LB) search algorithm. We demonstrate that setting this parameter ac ..."
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Cited by 4 (0 self)
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We apply an HMMbased text recognition system to the recognition of handwritten digit strings of unknown length. The algorithm is tailored to the input data by controlling the maximum number of levels searched by the Level Building (LB) search algorithm. We demonstrate that setting this parameter according to the pixel length of the observation sequence, rather than using a fixed value for all input data, results in a faster and more accurate system. Best results were achieved by setting the maximum number of levels to twice the estimated number of characters in the input string. We also describe experiments which show the potential for further improvement by using an adaptive termination criterion in the LB search. 1. Introduction Hidden Markov models (HMMs) [5] have been widely used in the field of speech recognition for many years [4], but have only recently begun to receive a similar degree of attention in the context of text recognition [1, 3]. The HMM approach is particularly su...
Progress Report: MultiAperture SAR Target Detection Using Hidden Markov Models
, 1994
"... This report highlights our current work and accomplishments on the project to exploit angular diversity for improved target detection in multiaperture SAR images. This report also contains a brief introduction to hidden Markov models, and identifies issues which we will resolve as work continues. W ..."
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This report highlights our current work and accomplishments on the project to exploit angular diversity for improved target detection in multiaperture SAR images. This report also contains a brief introduction to hidden Markov models, and identifies issues which we will resolve as work continues. We have analyzed multiaperture SAR images and demonstrated that anisotropic behavior is present in our multiaperture SAR image set. We performed baselining studies using the common method of CFAR LTT detection and formulated an HMM detection method using BaumWelch reestimation to train HMMs to represent target, tree clutter, and ground clutter pixels. Our results show that HMM detection produced significantly better results than CFAR LTT detection (with a 29by29 reference window) for the y This research was supported by Wright Laboratory. z The SPANN Lab's WWW URL is http://eewww.eng.ohiostate.edu/research/spann/. same multiaperture SAR image and requires less computation. Speci...
Supporting RealTime Analysis of Multimedia Communication Sessions
, 1992
"... We have developed a set of interactive tools for collecting, annotating, and analyzing group communication sessions. These tools have been used to model group meetings which we have enacted on our computerbased video conferencing system as well as single location meetings. The purpose of this work ..."
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Cited by 1 (0 self)
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We have developed a set of interactive tools for collecting, annotating, and analyzing group communication sessions. These tools have been used to model group meetings which we have enacted on our computerbased video conferencing system as well as single location meetings. The purpose of this work is to support the analysis of group meetings over computerbased video conferencing systems. The resulting analysis can be used for various purposes including creating meeting summaries, identifying communication patterns, facilitating group communication, and suggesting agendas for followon meetings. The current system is used for offline annotation and analysis of communication sessions which involve various parallel media tracks including the video and audio component for each participant, the text transcription of the meeting, and various documents and media forms referenced during the session. In this paper we review these tools and describe an architecture for employing these techniq...
The Automated Building and Updating of a Knowledge Base through the Analysis of Natural Language Text
, 1991
"... ..."
A TimeLength Constrained Level Building Algorithm for Large Vocabulary Handwritten Word Recognition
 IN PROC. 2ND INTERNATIONAL CONFERENCE ON ADVANCES IN PATTERN RECOGNITION
, 2001
"... In this paper we introduce a constrained Level Building Algorithm (LBA) in order to reduce the search space of a Large Vocabulary Handwritten Word Recognition (LVHWR) system. A time and a length constraint are introduced to limit the number of frames and the number of levels of the LBA respecti ..."
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Cited by 1 (1 self)
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In this paper we introduce a constrained Level Building Algorithm (LBA) in order to reduce the search space of a Large Vocabulary Handwritten Word Recognition (LVHWR) system. A time and a length constraint are introduced to limit the number of frames and the number of levels of the LBA respectively. A regression model that fits the response variables, namely, accuracy and speed, to a nonlinear function of the constraints is proposed and a statistical experimental design technique is employed to analyse the effects of the two constraints on the responses. Experimental results prove that the inclusion of these constraints improve the recognition speed of the LVHWR system without changing the recognition rate significantly.
ORIGINAL
"... Prototype learning for structured pattern representation applied to online recognition of handwritten Japanese characters ..."
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Prototype learning for structured pattern representation applied to online recognition of handwritten Japanese characters