Conferences related to Speech Language Processing

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2023 Annual International Conference of the IEEE Engineering in Medicine & Biology Conference (EMBC)

The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted full papers will be peer reviewed. Accepted high quality papers will be presented in oral and poster sessions,will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE.


ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

The ICASSP meeting is the world's largest and most comprehensive technical conference focused on signal processing and its applications. The conference will feature world-class speakers, tutorials, exhibits, and over 50 lecture and poster sessions.


2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

HRI is a highly selective annual conference that showcases the very best research and thinking in human-robot interaction. HRI is inherently interdisciplinary and multidisciplinary, reflecting work from researchers in robotics, psychology, cognitive science, HCI, human factors, artificial intelligence, organizational behavior, anthropology, and many other fields.

  • 2018 13th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

    HRI is a highly selective annual conference that showcases the very best research and thinking in human-robot interaction. HRI is inherently interdisciplinary and multidisciplinary, reflecting work from researchersin robotics, psychology, cognitive science, HCI, human factors, artificial intelligence, organizational behavior,anthropology, and many other fields.

  • 2017 12th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

    The conference serves as the primary annual meeting for researchers in the field of human-robot interaction. The event will include a main papers track and additional sessions for posters, demos, and exhibits. Additionally, the conference program will include a full day of workshops and tutorials running in parallel.

  • 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

    This conference focuses on the interaction between humans and robots.

  • 2015 10th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

    HRI is a single -track, highly selective annual conference that showcases the very bestresearch and thinking in human -robot interaction. HRI is inherently interdisciplinary and multidisciplinary,reflecting work from researchers in robotics, psychology, cognitive science, HCI, human factors, artificialintelligence, organizational behavior, anthropology, and many other fields.

  • 2014 9th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

    HRI is a highly selective annual conference that showcases the very best research and thinking in human -robot interaction. HRI is inherently interdisciplinary and multidisciplinary, reflecting work from researchers in robotics, psychology, cognitive science, HCI, human factors, artificial intelligence, organizational behavior, anthropology, and many other fields.

  • 2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

    HRI is a single -track, highly selective annual conference that showcases the very best research and thinking in human-robot interaction. HRI is inherently interdisciplinary and multidisciplinary, reflecting work from researchers in robotics, psychology, cognitive science, HCI, human factors, artificial intelligence, organizational behavior, anthropology, and many other fields.

  • 2012 7th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

    HRI is a single-track, highly selective annual conference that showcases the very best research and thinking in human-robot interaction. HRI is inherently interdisciplinary and multidisciplinary, reflecting work from researchers in robotics, psychology, cognitive science, HCI, human factors, artificial intelligence, organizational behavior, anthropology, and many other fields.

  • 2011 6th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

    Robot companions Lifelike robots Assistive (health & personal care) robotics Remote robots Mixed initiative interaction Multi-modal interaction Long-term interaction with robots Awareness and monitoring of humans Task allocation and coordination Autonomy and trust Robot-team learning User studies of HRI Experiments on HRI collaboration Ethnography and field studies HRI software architectures HRI foundations Metrics for teamwork HRI group dynamics.

  • 2010 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

    TOPICS: Robot companions, Lifelike robots, Assistive (health & personal care) robotics, Remote robots, Mixed initiative interaction, Multi-modal interaction, Long-term interaction with robots, Awareness and monitoring of humans, Task allocation and coordination, Autonomy and trust, Robot-team learning, User studies of HRI, Experiments on HRI collaboration, Ethnography and field studies, HRI software architectures

  • 2009 4th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

    * Robot companions * Lifelike robots * Assistive (health & personal care) robotics * Remote robots * Mixed initiative interaction * Multi-modal interaction * Long-term interaction with robots * Awareness and monitoring of humans * Task allocation and coordination * Autonomy and trust * Robot-team learning * User studies of HRI * Experiments on HRI collaboration * Ethnography and field studies * HRI software architectures

  • 2008 3rd ACM/IEEE International Conference on Human-Robot Interaction (HRI)

    Robot companions Lifelike robots Assistive (health & personal care) robotics Remote robots Mixed initiative interaction Multi-modal interaction Long-term interaction with robots Awareness and monitoring of humans Task allocation and coordination Autonomy and trust Robot-team learning User studies of HRI Experiments on HRI collaboration Ethnography and field studies HRI software architectures HRI foundations Metrics for teamwork HRI group dynamics Individual vs. group HRI

  • 2007 2nd Annual Conference on Human-Robot Interaction (HRI)


2018 Federated Conference on Computer Science and Information Systems (FedCSIS)

The mission of the FedCSIS Conference Series is to provide a highly acclaimed multi-conference forum in computer science and information systems. The forum invites researchers from around the world to contribute their research results and participate in Events focused on their scientific and professional interests in computer science and information systems.The FedCSIS multi-conference consists of a significant number of recurring Events and it welcomes proposals for new Events (conferences, symposia, workshops, special sessions). Each Event may run over any span of time within the conference dates (from half-day to three days). Since 2012, Proceedings of the FedCSIS conference are indexed in the Web of Science and other indexing services.

  • 2017 Federated Conference on Computer Science and Information Systems (FedCSIS)

    The FedCSIS Multiconference consists of Events (conferences, symposia, workshops, special sessions). Each Event may run over any span of time within the conference dates (from half-day to three days). The FedCSIS Events provide a platform for bringing together researchers, practitioners, and academia to present and discuss ideas, challenges and potential solutions on established or emerging topics related to research and practice in computer science and information systems. Since 2012, Proceedings of the FedCSIS conference are indexed in the Thomson Reuters Web of Science.

  • 2016 Federated Conference on Computer Science and Information Systems (FedCSIS)

    The FedCSIS Multiconference consists of Events (conferences, symposia, workshops, special sessions). Each Event may run over any span of time within the conference dates (from half-day to three days). The FedCSIS Events provide a platform for bringing together researchers, practitioners, and academia to present and discuss ideas, challenges and potential solutions on established or emerging topics related to research and practice in computer science and information systems. Since 2012, Proceedings of the FedCSIS conference are indexed in the Thomson Reuters Web of Science.

  • 2015 Federated Conference on Computer Science and Information Systems (FedCSIS)

    The FedCSIS Multiconference consists of Events (conferences, symposia, workshops, special sessions). Each Event may run over any span of time within the conference dates (from half-day to three days). The FedCSIS Events provide a platform for bringing together researchers, practitioners, and academia to present and discuss ideas, challenges and potential solutions on established or emerging topics related to research and practice in computer science and information systems.

  • 2014 Federated Conference on Computer Science and Information Systems (FedCSIS)

    The FedCSIS Multiconference consists of Events (conferences, symposia, workshops, special sessions). Each Event may run over any span of time within the conference dates (from half-day to three days). The FedCSIS Events provide a platform for bringing together researchers, practitioners, and academia to present and discuss ideas, challenges and potential solutions on established or emerging topics related to research and practice in computer science and information systems.

  • 2013 Federated Conference on Computer Science and Information Systems (FedCSIS)

    FedCSIS Multiconference consists of Events (conferences, symposia, workshops, special sessions) that provide a platform for bringing together researchers, practitioners, and academia to present and discuss ideas, challenges and potential solutions on topics related to research and practice in CS/IS.

  • 2012 Federated Conference on Computer Science and Information Systems (FedCSIS)

    Continuation of FedCSIS 2011, scope depends on the scopes of individual events that FedCSIS consists of.

  • 2011 Federated Conference on Computer Science and Information Systems (FedCSIS)


2018 IEEE Spoken Language Technology Workshop (SLT)

Speech Processing Technology and Applications

  • 2016 IEEE Spoken Language Technology Workshop (SLT)

    Speech Processing technology and applications

  • 2014 IEEE Spoken Language Technology Workshop (SLT)

    The goal of this workshop is to allow the speech and language processing community to share and present recent advances in various areas of spoken language technology, including but not limited to speech and language processing, spoken language processing, spoken language understanding, spoken dialog systems, speech translation, spoken document retrieval, human machine interaction, natural language processing, question answering from speech, speaker and language recognition, speech recognition and synthesis.

  • 2012 IEEE Spoken Language Technology Workshop (SLT 2012)

    The goal of the SLT workshop is to bring the speech and language communities together to discuss and present advances and challenges in the area of spoken language processing.

  • 2010 IEEE Spoken Language Technology Workshop (SLT 2010)

    The goal of the SLT workshop is to bring the speech and language communities together to discuss and present advances and challenges in the area of spoken language processing.

  • 2008 IEEE Spoken Language Technology Workshop (SLT 2008)

    The goal of this workshop is to bring the speech processing and natural language processing communities together to share and present recent advances in the area of spoken language technology, and to discuss and foster new research in this area.

  • 2006 IEEE Spoken Language Technology Workshop (SLT 2006)


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Periodicals related to Speech Language Processing

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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; ...


Computer

Computer, the flagship publication of the IEEE Computer Society, publishes peer-reviewed technical content that covers all aspects of computer science, computer engineering, technology, and applications. Computer is a resource that practitioners, researchers, and managers can rely on to provide timely information about current research developments, trends, best practices, and changes in the profession.


Computer Graphics and Applications, IEEE

IEEE Computer Graphics and Applications (CG&A) bridges the theory and practice of computer graphics. From specific algorithms to full system implementations, CG&A offers a strong combination of peer-reviewed feature articles and refereed departments, including news and product announcements. Special Applications sidebars relate research stories to commercial development. Cover stories focus on creative applications of the technology by an artist or ...


Information Forensics and Security, IEEE Transactions on

Research on the fundamental contributions and the mathematics behind information forensics, information seurity, surveillance, and systems applications that incorporate these features.


Intelligent Systems, IEEE

IEEE Intelligent Systems, a bimonthly publication of the IEEE Computer Society, provides peer-reviewed, cutting-edge articles on the theory and applications of systems that perceive, reason, learn, and act intelligently. The editorial staff collaborates with authors to produce technically accurate, timely, useful, and readable articles as part of a consistent and consistently valuable editorial product. The magazine serves software engineers, systems ...


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Most published Xplore authors for Speech Language Processing

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Xplore Articles related to Speech Language Processing

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Automatic Domain Specific Terminology Extraction using a Decision Support System

2006 ITI 4th International Conference on Information & Communications Technology, 2006

Speech languages or natural languages contents are major tools of communication. This research paper presents a natural language processing based automated system for understanding speech language text. A new rule based model is presented for analyzing the natural languages and extracting the relative meanings from the given text. User writes the natural language scenario in simple English in a few ...


Discriminative linear-transform based adaptation using minimum verification error

2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010

This paper presents an investigation of the minimum verification error linear regression (MVELR) method for discriminative linear-transform based adaptation. The MVE criterion is employed to estimate a set of discriminative linear transformations which achieve the smallest empirical average loss with the given adaptation data. The MVELR directly minimizes the total detection errors, some of which are results of characteristic mismatch ...


Discriminative Training for direct minimization of deletion, insertion and substitution errors

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011

In this paper, we follow the minimum error principle for acoustic modeling and formulate error objectives in insertion, deletion, and substitution separately for minimization during training. This new training paradigm generalized from the MVE criterion can explain the direct relationship between recognition errors and detection errors by re-interpreting deletion, insertion, and substitution errors as miss, false alarm, and miss/false-alarm errors ...


Parameter Estimation of Statistical Models Using Convex Optimization

IEEE Signal Processing Magazine, 2010

Discriminative learning methods have achieved many successes in speech and language processing during the past decades. Discriminative learning of generative models is a typical optimization problem, where efficient optimization methods play a critical role. For many widely used statistical models, discriminative learning normally leads to nonconvex optimization problems. In this article we used three representative examples to showcase how to ...


The Heterogeneous Deep Neural Network Processor With a Non-von Neumann Architecture

Proceedings of the IEEE, None

Today's CPUs are general-purpose processors, which have the von Neumann architecture (including the Harvard architectures) to maximize the generality and programmability. On the other hand, application-specific integrated circuits (ASICs) have domain-specific architectures to optimize the cost- effective performance but show very low generality. The combination of generality and ASIC, which usually seemed to have no contact, is expected to be ...


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Educational Resources on Speech Language Processing

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IEEE-USA E-Books

  • Automatic Domain Specific Terminology Extraction using a Decision Support System

    Speech languages or natural languages contents are major tools of communication. This research paper presents a natural language processing based automated system for understanding speech language text. A new rule based model is presented for analyzing the natural languages and extracting the relative meanings from the given text. User writes the natural language scenario in simple English in a few paragraphs and the designed system has an obvious capability of analyzing the given script by the user. After composite analysis and extraction of associated information, the designed system gives particular meanings to an assortment of speech language text on the basis of its context. The designed system uses standard speech language rules that are clearly defined for all speech languages as English, Urdu, Chinese, Arabic, French, ...etc. The designed system provides a quick and reliable way to comprehend speech language context and generates respective meanings. The application with such abilities can be more intelligent and pertinent specifically for the user to save the time.

  • Discriminative linear-transform based adaptation using minimum verification error

    This paper presents an investigation of the minimum verification error linear regression (MVELR) method for discriminative linear-transform based adaptation. The MVE criterion is employed to estimate a set of discriminative linear transformations which achieve the smallest empirical average loss with the given adaptation data. The MVELR directly minimizes the total detection errors, some of which are results of characteristic mismatch in the given adaptation data. In this study, segment-based phonetic detectors reflecting an important processing layer in speech event detection are initially trained via the conventional maximum likelihood (ML) method and then refined via the general MVE method using the original training data. Then, the initial MVE- trained detectors are adapted by two kinds of adaption techniques, MLLR and MVELR, respectively, with the given adaptation data for comparison. The experiments are performed on a supervised adaptation scenario and the effectiveness of the adapted detectors is evaluated based on the total detection error. Experimental results confirm the proposed MVELR method considerably reduces the total error rate over all categories of the detectors compared to the MLLR.

  • Discriminative Training for direct minimization of deletion, insertion and substitution errors

    In this paper, we follow the minimum error principle for acoustic modeling and formulate error objectives in insertion, deletion, and substitution separately for minimization during training. This new training paradigm generalized from the MVE criterion can explain the direct relationship between recognition errors and detection errors by re-interpreting deletion, insertion, and substitution errors as miss, false alarm, and miss/false-alarm errors happening together. Under the MVE criterion, by applying two mis-verification measures for miss and false alarm errors selectively along with the types of recognition error definition, we developed three individual objective training criteria, minimum deletion error (MDE), minimum insertion error (MIE), and minimum substitution error (MSE), of which each objective function can directly minimize each of the three types of the recognition errors. In the TIMIT phone recognition task, the experimental results confirm that each objective criterion of MDE, MIE, and MSE results in primarily minimizing its target error type, respectively. Furthermore, a simple combination of the individual objective criteria outperforms the conventional string-based MCE in the overall recognition error rate.

  • Parameter Estimation of Statistical Models Using Convex Optimization

    Discriminative learning methods have achieved many successes in speech and language processing during the past decades. Discriminative learning of generative models is a typical optimization problem, where efficient optimization methods play a critical role. For many widely used statistical models, discriminative learning normally leads to nonconvex optimization problems. In this article we used three representative examples to showcase how to use a proper convex relaxation method to convert discriminative learning of HMMs and MMMs into standard convex optimization problem so that it can be solved effectively and efficiently even for large-scale statistical models. We believe convex optimization will continue to play important role in discriminative learning of other statistical models in other application domains, such as statistical machine translation, computer vision, biometrics, and informatics.

  • The Heterogeneous Deep Neural Network Processor With a Non-von Neumann Architecture

    Today's CPUs are general-purpose processors, which have the von Neumann architecture (including the Harvard architectures) to maximize the generality and programmability. On the other hand, application-specific integrated circuits (ASICs) have domain-specific architectures to optimize the cost- effective performance but show very low generality. The combination of generality and ASIC, which usually seemed to have no contact, is expected to be enabled by deep learning (DL). DL, realized with deep neural networks (DNNs), has changed the paradigm of machine learning (ML) and brought significant progress in vision, speech, language processing, and many other applications. DNNs have special features that can be efficiently implemented with dedicated architectures, ASICs. Sharing their special features, DNNs have a wide variety of network architectures, and even the same network architecture can be used for different applications depending on the weight parameters. This paper aims to provide the necessity, validity, and characteristics of the ML-specific integrated circuits (MSICs) that have a different architecture from the von Neumann architecture. MSICs can avoid the overhead from the complex instruction set, instruction decoder, multilevel caches, and branch prediction of the recent von Neumann architecture processors designed for high generality and programmability. We will also discuss the necessity and validity of a heterogeneous architecture in MSIC, starting from the differences between the visual-type information processing and the vector-type information processing, and show the chip implementation results.

  • [Title page i]

    The following topics are dealt with: AI in databases and data mining; vision processing/understanding; feature selection; trends in SAT and CSP; AI in robotics; constraint programming; knowledge-based systems; case-based reasoning; intelligent Internet agents; speech/language processing and understanding; evolutionary computing; AI in games; planning and scheduling; AI algorithms; AI in software engineering and collaborative software agents; model-assistive sensing system; AI in biomedical engineering; etc.



Standards related to Speech Language Processing

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Jobs related to Speech Language Processing

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