Conferences related to Speech Recognition

Back to Top

2013 7th Conference on Speech Technology and Human - Computer Dialogue (SpeD 2013)

“SpeD 2013” will bring together academics and industry professionals from universities, government agencies and companies to present their achievements in speech technology and related fields. “SpeD 2013” is a conference and international forum which will reflect some of the latest tendencies in spoken language technology and human-computer dialogue research as well as some of the most recent applications in this area.

  • 2011 6th Conference on Speech Technology and Human - Computer Dialogue (SpeD 2011)

    SpeD 2011 will bring together academics and industry professionals from universities, government agencies and companies to present their achievements and the latest tendencies in spoken language technology and human-computer dialogue research as well as some of the most recent applications in this area.

  • 2009 5th Conference on Speech Technology and Human - Computer Dialogue (SpeD 2009)

    The 5th Conference on Speech Technology and Human-Computer Dialogue (at Constanta, Romania) brings together academics and industry professionals from universities, government agencies and companies to present their achievements in speech technology and related fields. SpeD 2009 is a conference and international forum which will reflect some of the latest tendencies in spoken language technology and human-computer dialogue research as well as some of the most recent applications in this area.


2013 Humaine Association Conference on Affective Computing and Intelligent Interaction (ACII)

The conference will address, but is not limited to, the following topics:• Computational and psychological models of emotion;• Affect in arts entertainment and multimedia;• Bodily manifestations of affect (facial expressions, posture, behavior, physiology);• Databases for emotion processing, development and issues;• Affective interfaces and applications (games, learning, dialogue systems…);• Ecological and continuous emotion assessment;• Affect in social interactions.

  • 2009 3rd International Conference on Affective Computing and Intelligent Interaction (ACII 2009)

    The conference series on Affective Computing and Intelligent Interaction is the premier international forum for state of the art in research on affective and multi modal human-machine interaction and systems. Every other year the ACII conference plays an important role in shaping related scientific, academic, and higher education programs. This year, we are especially soliciting papers discussing Enabling Behavioral and Socially-Aware Human-Machine Interfaces in areas including psychology.


2013 IEEE International Conference on Multimedia and Expo (ICME)

To promote the exchange of the latest advances in multimedia technologies, systems, and applications from both the research and development perspectives of the circuits and systems, communications, computer, and signal processing communities.

  • 2012 IEEE International Conference on Multimedia and Expo (ICME)

    IEEE International Conference on Multimedia & Expo (ICME) has been the flagship multimedia conference sponsored by four IEEE Societies. It exchanges the latest advances in multimedia technologies, systems, and applications from both the research and development perspectives of the circuits and systems, communications, computer, and signal processing communities.

  • 2011 IEEE International Conference on Multimedia and Expo (ICME)

    Speech, audio, image, video, text processing Signal processing for media integration 3D visualization, animation and virtual reality Multi-modal multimedia computing systems and human-machine interaction Multimedia communications and networking Multimedia security and privacy Multimedia databases and digital libraries Multimedia applications and services Media content analysis and search Hardware and software for multimedia systems Multimedia standards and related issues Multimedia qu

  • 2010 IEEE International Conference on Multimedia and Expo (ICME)

    A flagship multimedia conference sponsored by four IEEE societies, ICME serves as a forum to promote the exchange of the latest advances in multimedia technologies, systems, and applications from both the research and development perspectives of the circuits and systems, communications, computer, and signal processing communities.

  • 2009 IEEE International Conference on Multimedia and Expo (ICME)

    IEEE International Conference on Multimedia & Expo is a major annual international conference with the objective of bringing together researchers, developers, and practitioners from academia and industry working in all areas of multimedia. ICME serves as a forum for the dissemination of state-of-the-art research, development, and implementations of multimedia systems, technologies and applications.


2013 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU)

The ASRU workshop meets every two years and has a tradition of bringing together researchers from academia and industry in an intimate and collegial setting to discuss problems of common interest in automatic speech recognition and understanding.


2013 International Carnahan Conference on Security Technology (ICCST)

This international conference is a forum for all aspects of physical, cyber and electronic security research, development, systems engineering, testing, evaluation, operations and sustainability. The ICCST facilitates the exchange of ideas and information.

  • 2012 IEEE International Carnahan Conference on Security Technology (ICCST)

    Research, development, and user aspects of security technology, including principles of operation, applications, and user experiences.

  • 2011 International Carnahan Conference on Security Technology (ICCST)

    This annual conference is the world s longest -running, international technical symposium on security technology. This conference is a forum for collaboration on all aspects of physical, cyber and electronic security research, development, systems engineering, testing, evaluation, operations and sustainment. The ICCST facilitates the exchange of ideas and sharing of information on both new and existing technology and systems. Conference participants are encouraged to consider the impact of their work on society. The ICCST provides a foundation for support to authorities and agencies responsible for security, safety and law enforcement in the use of available and future technology.

  • 2010 IEEE International Carnahan Conference on Security Technology (ICCST)

    The ICCST is a forum for researchers and practitioners in both new and existing security technology, providing an interchange of knowledge through paper presentations and publication of proceedings that have been selected by the ICCST organizing committee.

  • 2009 International Carnahan Conference on Security Technology (ICCST)

    Conference is directed toward research and development and user aspects of electronic security technology.

  • 2008 International Carnahan Conference on Security Technology (ICCST)

    The ICCST is directed toward the research and development aspects of electronic security technology, including the operational testing of the technology. It establishes a forum for the exchange of ideas and dissemination of information on both new and existing technology. Conference participants are stimulated to consider the impact of their work on society. The Conference is an interchange of knowledge through the presentation of learned papers that have been selected by the ICCST organizing committee.

  • 2007 IEEE International Carnahan Conference on Security Technology (ICCST)

  • 2006 IEEE International Carnahan Conference on Security Technology (ICCST)


More Conferences

Periodicals related to Speech Recognition

Back to Top

Audio, Speech, and Language Processing, IEEE Transactions on

Speech analysis, synthesis, coding speech recognition, speaker recognition, language modeling, speech production and perception, speech enhancement. In audio, transducers, room acoustics, active sound control, human audition, analysis/synthesis/coding of music, and consumer audio. (8) (IEEE Guide for Authors) The scope for the proposed transactions includes SPEECH PROCESSING - Transmission and storage of Speech signals; speech coding; speech enhancement and noise reduction; ...


Pattern Analysis and Machine Intelligence, IEEE Transactions on

Statistical and structural pattern recognition; image analysis; computational models of vision; computer vision systems; enhancement, restoration, segmentation, feature extraction, shape and texture analysis; applications of pattern analysis in medicine, industry, government, and the arts and sciences; artificial intelligence, knowledge representation, logical and probabilistic inference, learning, speech recognition, character and text recognition, syntactic and semantic processing, understanding natural language, expert systems, ...


Selected Areas in Communications, IEEE Journal on

All telecommunications, including telephone, telegraphy, facsimile, and point-to-point television, by electromagnetic propagation, including radio; wire; aerial, underground, coaxial, and submarine cables; waveguides, communication satellites, and lasers; in marine, aeronautical, space, and fixed station services; repeaters, radio relaying, signal storage, and regeneration; telecommunication error detection and correction; multiplexing and carrier techniques; communication switching systems; data communications; communication theory; and wireless communications.


Systems, Man and Cybernetics, Part A, IEEE Transactions on

Systems engineering, including efforts that involve issue formnaulations, issue analysis and modeling, and decision making and issue interpretation at any of the life-cycle phases associated with the definition, development, and implementation of large systems. It will also include efforts that relate to systems management, systems engineering processes and a variety of systems engineering methods such as optimization, modeling and simulation. ...



Most published Xplore authors for Speech Recognition

Back to Top

Xplore Articles related to Speech Recognition

Back to Top

Dialect/Accent Classification Using Unrestricted Audio

Rongqing Huang; John H. L. Hansen; Pongtep Angkititrakul IEEE Transactions on Audio, Speech, and Language Processing, 2007

This study addresses novel advances in English dialect/accent classification. A word-based modeling technique is proposed that is shown to outperform a large vocabulary continuous speech recognition (LVCSR)-based system with significantly less computational costs. The new algorithm, which is named Word-based Dialect Classification (WDC), converts the text-independent decision problem into a text-dependent decision problem and produces multiple combination decisions at the ...


A comparison of different clustering algorithms for speech recognition

J. Goddard; A. E. Martinez; F. M. Martinez; T. Aljama Proceedings of the 43rd IEEE Midwest Symposium on Circuits and Systems (Cat.No.CH37144), 2000

K-means and SOM have been frequently applied to clustering problems in speech recognition. Recently, new clustering algorithms have been introduced which present certain advantages over both of them. The present paper compares the performance of one of these, STVQ, to k-means and SOM on two well-known speech data sets


Blind separation for mixtures of sub-Gaussian and super-Gaussian sources

B. -C. Ihm; D. -J. Park; Y. -H. Kwon 2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353), 2000

We propose a new intelligent blind source separation algorithm for the mixture of sub-Gaussian and super-Gaussian sources. The algorithm consists of an update equation of the separating matrix and an adjustment equation of nonlinear functions. The weighted sum of two nonlinear functions is adapted to obtain the proper nonlinear function for each source. To verify the validity of the proposed ...


Using 3G Smartphones for MALL

Wang Yan; Wu Liping 2013 Fourth International Conference on Intelligent Systems Design and Engineering Applications, 2013

Mobile Assisted Language Learning (MALL) is the latest development of Mobile Learning (m-learning) and Computer-assisted Language Learning (CALL). This paper will discuss ways in which 3G smartphones can be used for MALL. The built-in functions of 3G smartphones, along with the various Web Apps and Native Apps that can be used to enhance these functions, provide a wide variety of ...


Optimized speaker independent speech recognition system for low cost application

C. C. Teh; C. C. Jong; L. Siek; K. K. Loa Proceedings of the 43rd IEEE Midwest Symposium on Circuits and Systems (Cat.No.CH37144), 2000

Describes an ASIC design and implementation of 15-isolated-word, speaker independent speech recognition system for low cost application. The input to the IC is a 12-bit sample with a sampling rate of 11.025 kHz. The delay between the end of a word and the response from the IC is approximately 0.24s. The IC runs at 10 MHz system clock and is ...


More Xplore Articles

Educational Resources on Speech Recognition

Back to Top

eLearning

Dialect/Accent Classification Using Unrestricted Audio

Rongqing Huang; John H. L. Hansen; Pongtep Angkititrakul IEEE Transactions on Audio, Speech, and Language Processing, 2007

This study addresses novel advances in English dialect/accent classification. A word-based modeling technique is proposed that is shown to outperform a large vocabulary continuous speech recognition (LVCSR)-based system with significantly less computational costs. The new algorithm, which is named Word-based Dialect Classification (WDC), converts the text-independent decision problem into a text-dependent decision problem and produces multiple combination decisions at the ...


A comparison of different clustering algorithms for speech recognition

J. Goddard; A. E. Martinez; F. M. Martinez; T. Aljama Proceedings of the 43rd IEEE Midwest Symposium on Circuits and Systems (Cat.No.CH37144), 2000

K-means and SOM have been frequently applied to clustering problems in speech recognition. Recently, new clustering algorithms have been introduced which present certain advantages over both of them. The present paper compares the performance of one of these, STVQ, to k-means and SOM on two well-known speech data sets


Blind separation for mixtures of sub-Gaussian and super-Gaussian sources

B. -C. Ihm; D. -J. Park; Y. -H. Kwon 2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353), 2000

We propose a new intelligent blind source separation algorithm for the mixture of sub-Gaussian and super-Gaussian sources. The algorithm consists of an update equation of the separating matrix and an adjustment equation of nonlinear functions. The weighted sum of two nonlinear functions is adapted to obtain the proper nonlinear function for each source. To verify the validity of the proposed ...


Using 3G Smartphones for MALL

Wang Yan; Wu Liping 2013 Fourth International Conference on Intelligent Systems Design and Engineering Applications, 2013

Mobile Assisted Language Learning (MALL) is the latest development of Mobile Learning (m-learning) and Computer-assisted Language Learning (CALL). This paper will discuss ways in which 3G smartphones can be used for MALL. The built-in functions of 3G smartphones, along with the various Web Apps and Native Apps that can be used to enhance these functions, provide a wide variety of ...


Optimized speaker independent speech recognition system for low cost application

C. C. Teh; C. C. Jong; L. Siek; K. K. Loa Proceedings of the 43rd IEEE Midwest Symposium on Circuits and Systems (Cat.No.CH37144), 2000

Describes an ASIC design and implementation of 15-isolated-word, speaker independent speech recognition system for low cost application. The input to the IC is a 12-bit sample with a sampling rate of 11.025 kHz. The delay between the end of a word and the response from the IC is approximately 0.24s. The IC runs at 10 MHz system clock and is ...


More eLearning Resources

IEEE-USA E-Books

  • Learning Under Covariate Shift

    As the power of computing has grown over the past few decades, the field of machine learning has advanced rapidly in both theory and practice. Machine learning methods are usually based on the assumption that the data generation mechanism does not change over time. Yet real-world applications of machine learning, including image recognition, natural language processing, speech recognition, robot control, and bioinformatics, often violate this common assumption. Dealing with non-stationarity is one of modern machine learning's greatest challenges. This book focuses on a specific non-stationary environment known as covariate shift, in which the distributions of inputs (queries) change but the conditional distribution of outputs (answers) is unchanged, and presents machine learning theory, algorithms, and applications to overcome this variety of non-stationarity. After reviewing the state-of- the-art research in the field, the authors discuss topics that include learning under covariate shift, model selection, importance estimation, and active learning. They describe such real world applications of covariate shift adaption as brain-computer interface, speaker identification, and age prediction from facial images. With this book, they aim to encourage future research in machine learning, statistics, and engineering that strives to create truly autonomous learning machines able to learn under non- stationarity.

  • Intelligent Speech/Audio Processing for Multimedia Applications

    Intelligent speech and audio processing can provide efficient and smart interfaces for various multimedia applications. Generally, speech is the most natural form of human communication. Audio and music can enhance our emotional impacts and promote interest in multimedia applications. A successful interactive multimedia system must have the capabilities of speech and audio compression, text-to-speech conversion, speech understanding, and music synthesis. The main purpose of speech and audio compression is to provide cost-effective storage or to minimize transmission costs. Text-to-speech converts linguistic information stored as data or text into speech for the applications of talking terminals, alarm systems, and audiotext services. Speech understanding systems make it possible for people to interact with computers using human speech. Its success relies on the integration of a wide variety of speech technologies, including acoustic, lexical, syntactic, semantic, and pragmatic analyses. The applications of music processing for multimedia were mostly realized by means of the combination of music, graphics, video, and other media. Since musical sounds and compositions can be precisely specified and controlled by a computer, we can easily create artificial orchestras, performers, and composers. Nowadays, multimedia systems have become more sophisticated with the advances made in computer and microelectronic technologies. Many applications require efficient processing of speech and audio for interactive presentations and integration with other types of media. The application-specific hardwares are proposed to meet the high-speed, low-cost, lightweight, and low-power requirements. The design example of a speech recognition processor and system for voice-control applications is introduced. The industrial standards and commercial products of speech and audio processing ar e also summarized in this chapter.

  • Appendix: List of Symbols and Abbreviations

    As the power of computing has grown over the past few decades, the field of machine learning has advanced rapidly in both theory and practice. Machine learning methods are usually based on the assumption that the data generation mechanism does not change over time. Yet real-world applications of machine learning, including image recognition, natural language processing, speech recognition, robot control, and bioinformatics, often violate this common assumption. Dealing with non-stationarity is one of modern machine learning's greatest challenges. This book focuses on a specific non-stationary environment known as covariate shift, in which the distributions of inputs (queries) change but the conditional distribution of outputs (answers) is unchanged, and presents machine learning theory, algorithms, and applications to overcome this variety of non-stationarity. After reviewing the state-of- the-art research in the field, the authors discuss topics that include learning under covariate shift, model selection, importance estimation, and active learning. They describe such real world applications of covariate shift adaption as brain-computer interface, speaker identification, and age prediction from facial images. With this book, they aim to encourage future research in machine learning, statistics, and engineering that strives to create truly autonomous learning machines able to learn under non- stationarity.

  • No title

    Speech dynamics refer to the temporal characteristics in all stages of the human speech communication process. This speech "chain" starts with the formation of a linguistic message in a speaker's brain and ends with the arrival of the message in a listener's brain. Given the intricacy of the dynamic speech process and its fundamental importance in human communication, this monograph is intended to provide a comprehensive material on mathematical models of speech dynamics and to address the following issues: How do we make sense of the complex speech process in terms of its functional role of speech communication? How do we quantify the special role of speech timing? How do the dynamics relate to the variability of speech that has often been said to seriously hamper automatic speech recognition? How do we put the dynamic process of speech into a quantitative form to enable detailed analyses? And finally, how can we incorporate the knowledge of speech dynamics into compu erized speech analysis and recognition algorithms? The answers to all these questions require building and applying computational models for the dynamic speech process. What are the compelling reasons for carrying out dynamic speech modeling? We provide the answer in two related aspects. First, scientific inquiry into the human speech code has been relentlessly pursued for several decades. As an essential carrier of human intelligence and knowledge, speech is the most natural form of human communication. Embedded in the speech code are linguistic (as well as para-linguistic) messages, which are conveyed through four levels of the speech chain. Underlying the robust encoding and transmission of the linguistic messages are the speech dynamics at all the four levels. Mathematical modeling of speech dynamics provides an effective tool in the scientific methods of studying the speech chain. Such scientific studies help understand why humans speak as they do and how humans exploit redundanc and variability by way of multitiered dynamic processes to enhance the efficiency and effectiveness of human speech communication. Second, advancement of human language technology, especially that in automatic recognition of natural-style human speech is also expected to benefit from comprehensive computational modeling of speech dynamics. The limitations of current speech recognition technology are serious and are well known. A commonly acknowledged and frequently discussed weakness of the statistical model underlying current speech recognition technology is the lack of adequate dynamic modeling schemes to provide correlation structure across the temporal speech observation sequence. Unfortunately, due to a variety of reasons, the majority of current research activities in this area favor only incremental modifications and improvements to the existing HMM-based state-of-the-art. For example, while the dynamic and correlation modeling is known to be an important topic, most of the sy tems nevertheless employ only an ultra-weak form of speech dynamics; e.g., differential or delta parameters. Strong-form dynamic speech modeling, which is the focus of this monograph, may serve as an ultimate solution to this problem. After the introduction chapter, the main body of this monograph consists of four chapters. They cover various aspects of theory, algorithms, and applications of dynamic speech models, and provide a comprehensive survey of the research work in this area spanning over past 20~years. This monograph is intended as advanced materials of speech and signal processing for graudate-level teaching, for professionals and engineering practioners, as well as for seasoned researchers and engineers specialized in speech processing

  • Speech Recognition in Multimedia HumanMachine Interfaces Using Neural Networks

    The past decade has been highlighted by the emerging technology of multimedia interface design. Intelligent multimedia interfaces can be developed that require very little computer sophistication on the part of the user. This chapter focuses on speech recognition systems applied to multimedia human- machine interfaces. There are a number of speech recognition systems on the market today, and some of them can be integrated into task-specific applications. However, speech recognition research still faces a few challenges in the area of multimedia human-machine interfaces. This chapter presents some approaches based on neural networks for Mandarin speech recognition. In practical applications, a robust Mandarin speech recognition system (VenusDictate) applied to multimedia interfaces is described.

  • Bibliography

    As the power of computing has grown over the past few decades, the field of machine learning has advanced rapidly in both theory and practice. Machine learning methods are usually based on the assumption that the data generation mechanism does not change over time. Yet real-world applications of machine learning, including image recognition, natural language processing, speech recognition, robot control, and bioinformatics, often violate this common assumption. Dealing with non-stationarity is one of modern machine learning's greatest challenges. This book focuses on a specific non-stationary environment known as covariate shift, in which the distributions of inputs (queries) change but the conditional distribution of outputs (answers) is unchanged, and presents machine learning theory, algorithms, and applications to overcome this variety of non-stationarity. After reviewing the state-of- the-art research in the field, the authors discuss topics that include learning under covariate shift, model selection, importance estimation, and active learning. They describe such real world applications of covariate shift adaption as brain-computer interface, speaker identification, and age prediction from facial images. With this book, they aim to encourage future research in machine learning, statistics, and engineering that strives to create truly autonomous learning machines able to learn under non- stationarity.

  • No title

    This book is about HCI research in an industrial research setting. It is based on the experiences of two researchers at the IBM T. J. Watson Research Center. Over the last two decades, Drs. John and Clare-Marie Karat have conducted HCI research to create innovative usable technology for users across a variety of domains. We begin the book by introducing the reader to the context of industrial research as well as a set of common themes or guidelines to consider in conducting HCI research in practice. Then case study examples of HCI approaches to the design and evaluation of usable solutions for people are presented and discussed in three domain areas: - item Conversational speech technologies, - item Personalization in eCommerce, and - item Security and privacy policy management technologies In each of the case studies, the authors illustrate and discuss examples of HCI approaches to design and evaluation that worked well and those that did not. They discuss what was learned over time bout different HCI methods in practice, and changes that were made to the HCI tools used over time. The Karats discuss trade-offs and issues related to time, resources, and money and the value derived from different HCI methods in practice. These decisions are ones that need to be made regularly in the industrial sector. Similarities and differences with the types of decisions made in this regard in academia will be discussed. The authors then use the context of the three case studies in the three research domains to draw insights and conclusions about the themes that were introduced in the beginning of the book. The Karats conclude with their perspective about the future of HCI industrial research. Table of Contents: Introduction: Themes and Structure of the Book / Case Study 1: Conversational Speech Technologies: Automatic Speech Recognition (ASR) / Case Study 2: Personalization in eCommerce / Case Study 3: Security and Privacy Policy Management Technologies / Insights and Conclusio s / The Future of Industrial HCI Research

  • Index

    As the power of computing has grown over the past few decades, the field of machine learning has advanced rapidly in both theory and practice. Machine learning methods are usually based on the assumption that the data generation mechanism does not change over time. Yet real-world applications of machine learning, including image recognition, natural language processing, speech recognition, robot control, and bioinformatics, often violate this common assumption. Dealing with non-stationarity is one of modern machine learning's greatest challenges. This book focuses on a specific non-stationary environment known as covariate shift, in which the distributions of inputs (queries) change but the conditional distribution of outputs (answers) is unchanged, and presents machine learning theory, algorithms, and applications to overcome this variety of non-stationarity. After reviewing the state-of- the-art research in the field, the authors discuss topics that include learning under covariate shift, model selection, importance estimation, and active learning. They describe such real world applications of covariate shift adaption as brain-computer interface, speaker identification, and age prediction from facial images. With this book, they aim to encourage future research in machine learning, statistics, and engineering that strives to create truly autonomous learning machines able to learn under non- stationarity.

  • Epilogue: Siri ... What's the Meaning of Life?

    Stanley Kubrick's 1968 film 2001: A Space Odyssey famously featured HAL, a computer with the ability to hold lengthy conversations with his fellow space travelers. More than forty years later, we have advanced computer technology that Kubrick never imagined, but we do not have computers that talk and understand speech as HAL did. Is it a failure of our technology that we have not gotten much further than an automated voice that tells us to "say or press 1"? Or is there something fundamental in human language and speech that we do not yet understand deeply enough to be able to replicate in a computer? In The Voice in the Machine, Roberto Pieraccini examines six decades of work in science and technology to develop computers that can interact with humans using speech and the industry that has arisen around the quest for these technologies. He shows that although the computers today that understand speech may not have HAL's capacity for conversation, they have capabilities that make them usable in many applications today and are on a fast track of improvement and innovation. Pieraccini describes the evolution of speech recognition and speech understanding processes from waveform methods to artificial intelligence approaches to statistical learning and modeling of human speech based on a rigorous mathematical model--specifically, Hidden Markov Models (HMM). He details the development of dialog systems, the ability to produce speech, and the process of bringing talking machines to the market. Finally, he asks a question that only the future can answer: will we end up with HAL-like computers or something completely unexpected?

  • Index

    Many important problems involve decision making under uncertainty -- that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance. Focusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables; utility theory as a framework for understanding optimal decision making under uncertainty; Markov decision processes as a me hod for modeling sequential problems; model uncertainty; state uncertainty; and cooperative decision making involving multiple interacting agents. A series of applications shows how the theoretical concepts can be applied to systems for attribute-based person search, speech applications, collision avoidance, and unmanned aircraft persistent surveillance. _Decision Making Under Uncertainty _unifies research from different communities using consistent notation, and is accessible to students and researchers across engineering disciplines who have some prior exposure to probability theory and calculus. It can be used as a text for advanced undergraduate and graduate students in fields including computer science, aerospace and electrical engineering, and management science. It will also be a valuable professional reference for researchers in a variety of disciplines.



Standards related to Speech Recognition

Back to Top

No standards are currently tagged "Speech Recognition"


Jobs related to Speech Recognition

Back to Top