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Research in the field of automatic speech and speaker recognition has made a number of significant advances in the last two decades, influenced by advances in signal processing, algorithms, architectures, and hardware. These advances include: the adoption of a statistical pattern recognition paradigm; the use of the hidden Markov modeling framework to characterize both the spectral and the temporal variations in the speech signal; the use of a large set of speech utterance examples from a large population of speakers to train the hidden Markov models of some fundamental speech units; the organization of speech and language knowledge sources into a structural finite state network; and the use...
The term speech processing refers to the scientific discipline concerned with the analysis and processing of speech signals for getting the best benefit in various practical scenarios. These different practical scenarios correspond to a large variety of applications of speech processing research. Examples of some applications include enhancement, coding, synthesis, recognition and speaker recognition. A very rapid growth, particularly during the past ten years, has resulted due to the efforts of many leading scientists. The ideal aim is to develop algorithms for a certain task that maximize performance, are computationally feasible and are robust to a wide class of conditions. The purpose of...
This book reflects decades of important research on the mathematical foundations of speech recognition. It focuses on underlying statistical techniques such as hidden Markov models, decision trees, the expectation-maximization algorithm, information theoretic goodness criteria, maximum entropy probability estimation, parameter and data clustering, and smoothing of probability distributions. The author's goal is to present these principles clearly in the simplest setting, to show the advantages of self-organization from real data, and to enable the reader to apply the techniques. Bradford Books imprint
This conference, organized jointly by UTC and INRIA, is the biennial general conference of the IFIP Technical Committee 7 (System Modelling and Optimization), and reflects the activity of its members and working groups. These proceedings contain a collection of papers (82 from the more than 400 submitted) as well as the plenary lectures presented at the conference.
This volume collects the papers accepted for presentation at the Second European Conference on Computer Vision, held in Santa Margherita Ligure, Italy, May 19-22, 1992. Sixteen long papers, 41 short papers and 48 posters were selected from 308 submissions. The contributions are structured into 14 sections reflecting the major research topics in computer vision currently investigated worldwide. The sections are entitled: features, color, calibration and matching, depth, stereo-motion, tracking, active vision, binocular heads, curved surfaces and objects, reconstruction and shape, recognition, and applications.
TheseproceedingscontainthepaperspresentedatPARA2002,theSixth- ternationalConferenceonAppliedParallelComputing. PARA2002washeldin Espoo,Finland,June15–18,2002,andhostedbyCSC,theFinnishinformation technologycenterforscience. Thegeneralthemeoftheconferencewasadvanced scienti?ccomputing. Theconferencedemonstratedtheabilityofadvancedscienti?ccomputingto solvereal-worldproblems,andhighlightedmethods,instruments,andtrendsin futurescienti?ccomputing. Theconferencebeganwithaone-daytutorialsession onGridprogramming. Theconferencefocusedonanapplication-oriented,multi-disciplinary,and multi-scaleapproach. Awidevarietyofscienti?ccomputingapplicationswere introduced,fromsemiconductorprocessingandbehaviorofthehumanbodyto oceanicandatmosphericphenomena. Scienti?ccomputingcoupledwithmulti-disciplinaryandmulti-scaleexp- tisewillplayasigni?cantroleinsolvingchallengingproblemsinscience.
Over the last 20 years, approaches to designing speech and language processing algorithms have moved from methods based on linguistics and speech science to data-driven pattern recognition techniques. These techniques have been the focus of intense, fast-moving research and have contributed to significant advances in this field. Pattern Reco
The rapid advancement of digital multimedia technologies has not only revolutionized the production and distribution of audiovisual content, but also created the need to efficiently analyze TV programs to enable applications for content managers and consumers. Leaving no stone unturned, TV Content Analysis: Techniques and Applications provides a de
"The central fact is that we are planning agents." (M. Bratman, Intentions, Plans, and Practical Reasoning, 1987, p. 2) Recent arguments to the contrary notwithstanding, it seems to be the case that people-the best exemplars of general intelligence that we have to date do a lot of planning. It is therefore not surprising that modeling the planning process has always been a central part of the Artificial Intelligence enterprise. Reasonable behavior in complex environments requires the ability to consider what actions one should take, in order to achieve (some of) what one wants and that, in a nutshell, is what AI planning systems attempt to do. Indeed, the basic description of a plan generation algorithm has remained constant for nearly three decades: given a desciption of an initial state I, a goal state G, and a set of action types, find a sequence S of instantiated actions such that when S is executed instate I, G is guaranteed as a result. Working out the details of this class of algorithms, and making the elabora tions necessary for them to be effective in real environments, have proven to be bigger tasks than one might have imagined.
Proceedings of the NATO Advanced Study Institute on Computational Models of Speech Pattern Processing, held in St. Helier, Jersey, UK, July 7-18, 1997