Conferences related to Speech Recognition

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2018 26th Signal Processing and Communications Applications Conference (SIU)

The general scope of the conference ranges from signal and image processing to telecommunication, and applications of signal processing methods in biomedical and communication problems.

  • 2017 25th Signal Processing and Communications Applications Conference (SIU)

    Signal Processing and Communication Applications (SIU) conference is the most prominent scientific meeting on signal processing in Turkey bringing together researchers working in signal processing and communication fields. Topics include but are not limited to the areas of research listed in the keywords.

  • 2016 24th Signal Processing and Communication Application Conference (SIU)

    Signal Processing Theory, Statistical Signal Processing, Nonlinear Signal Processing, Adaptive Signal Processing, Array and Multichannel Signal Processing, Signal Processing for Sensor Networks, Time-Frequency Analysis, Speech / Voice Processing and Recognition, Computer Vision, Pattern Recognition, Machine Learning for Signal Processing, Human-Machine Interaction, Brain-Computer Interaction, Signal-Image Acquisition and Generation, image Processing, video Processing, Image Printing and Presentation, Image / Video / Audio browsing and retrieval, Image / Video / Audio Watermarking, Multimedia Signal Processing, Biomedical Signal Processing and Image Processing, Bioinformatics, Biometric Signal-Image Processing and Recognition, Signal Processing for Security and Defense, Signal and Image Processing for Remote Sensing, Signal Processing Hardware, Signal Processing Education, Radar Signal Processing, Communication Theory, Communication Networks, Wireless Communications

  • 2015 23th Signal Processing and Communications Applications Conference (SIU)

    Signal Processing Theory Statistical Signal Processing Nonlinear Signal Processing Adaptive Signal Processing Array and Multichannel Signal Processing Signal Processing for Sensor Networks Time-Frequency Analysis Speech / Voice Processing and Recognition Computer Vision Pattern Recognition Machine Learning for Signal Processing Human-Machine Interaction Brain-Computer Interaction Signal-Image Acquisition and Generation image Processing video Processing Image Printing and Presentation Image / Video / Audio browsing and retrieval Image / Video / Audio Watermarking Multimedia Signal Processing Biomedical Signal Processing and Image Processing Bioinformatics Biometric Signal-Image Processing and Recognition Signal Processing for Security and Defense Signal and Image Processing for Remote Sensing Signal Processing Hardware Signal Processing Education Radar Signal Processing Communication Theory Communication Networks Wireless Communications

  • 2014 22nd Signal Processing and Communications Applications Conference (SIU)

    SIU will be held in Trabzon, Turkey at the Karadeniz Technical University Convention and Exhibition Centre on April 23, 2014. SIU is the largest and most comprehensive technical conference focused on signal processing and its applications in Turkey. Last year there were 500 hundred participants. The conference will feature renowned speakers, tutorials, and thematic workshops. Topics include but are not limited to: Signal Procesing, Image Processing, Communication, Computer Vision, Machine Learning, Biomedical Signal Processing,

  • 2013 21st Signal Processing and Communications Applications Conference (SIU)

    Conference will discuss state of the art solutions and research results on existing and future DSP and telecommunication systems, applications, and related standardization activities. Conference will also include invited lectures, tutorials and special sessions.

  • 2012 20th Signal Processing and Communications Applications Conference (SIU)

    Conference will discuss state of the art solutions and research results on existing and future DSP and telecommunication systems, applications, and related standardization activities. Conference will also include invited lectures, tutorials and special sessions.

  • 2011 19th Signal Processing and Communications Applications Conference (SIU)

    Conference will bring together academia and industry professionals as well as students and researchers to present and discuss state of the art solutions and research results on existing and future DSP and telecommunication systems, applications, and related standardization activities. The Conference will also include invited lectures, tutorials and special sessions.

  • 2010 IEEE 18th Signal Processing and Communications Applications Conference (SIU)

    S1.Theory of Signal-Processing S2.Statistical Signal-Processing S3.Multimedia Signal-Processing S4.Biomedical Signal-Processing S5.Sensor Networks S6.Multirate Signal-Processing S7.Pattern Recognition S8.Computer Vision S9.Adaptive Filters S10.Image/Video/Speech Browsing, Retrieval S11.Speech/Audio Coding S12.Speech Processing S13.Human-Machine Interfaces S14.Surveillance Signal Processing S15.Bioinformatics S16.Self-Learning S17.Signal-Processing Education S18.Signal-Processing Systems S1

  • 2009 IEEE 17th Signal Processing and Communications Applications Conference (SIU)

    The scope of the conference is to cover recent topics in theory and applications of Signal Processing and Communications.

  • 2008 IEEE 16th Signal Processing and Communications Applications Conference (SIU)

    Signal Processing, Image Processing, Speech Processing, Pattern Recognition, Human Computer Interaction, Communication, Video and Speech indexing, Computer Vision, Biomedical Signal Processing

  • 2007 IEEE 15th Signal Processing and Communications Applications (SIU)

  • 2006 IEEE 14th Signal Processing and Communications Applications (SIU)

  • 2005 IEEE 13th Signal Processing and Communications Applications (SIU)

  • 2004 IEEE 12th Signal Processing and Communications Applications (SIU)


2018 IEEE 61st International Midwest Symposium on Circuits and Systems (MWSCAS)

Analog Circuits, Digital VLSI Circuits, Neural Networks, Non-Linear System, Computer Aided Design, Communication Systems, Digital Signal Processing, MEMS, Nano-electronics


2018 IEEE International Conference on Multimedia and Expo (ICME)

The IEEE International Conference on Multimedia & Expo (ICME) has been the flagship multimedia conference sponsored by four IEEE societies since 2000. It 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. ICME also features an Exposition of multimedia products and prototypes.


2018 IEEE Seventh International Conference on Communications and Electronics (ICCE)

communications and electronics

  • 2016 IEEE Sixth International Conference on Communications and Electronics (ICCE)

    Contributed papers are solicited describing original works in electronics, communications engineering and related technologies. Topics and technical areas of interest include but are not limited to the following: 1. Communications Networks and Systems; 2. Signal Processing and Applications; 3. Microwave Engineering; 4. Electronics Systems. In addition, three special sessions are included in the scope of ICCE2016: 1. Software Defined Networking 2.Underwater Acoustic Communication3.Crowdsourcing and Crowdsourcing Application

  • 2014 IEEE Fifth International Conference on Communications and Electronics (ICCE)

    Contributed papers are solicited describing original works in electronics, communications engineering and related technologies. Topics and technical areas of interest include but are not limited to the following: Communications Networks & Systems; Signal Processing and Applications; Microwave Engineering; Electronics Systems; with 3 special sessions on: Software Defined Networking; Underwater Acoustic Communications; Crowdsourcing.

  • 2012 Fourth International Conference on Communications and Electronics (ICCE)

    ICCE 2012 looks for significant contributions to various topics in communication engineering, networking, microwave engineering, signal processing and electronics engineering. The conference will also include tutorials, workshops, and technology panels given by world-class speakers.

  • 2010 Third International Conference on Communications and Electronics (ICCE)

    ICCE 2010 will focus on cutting edge research, development, and applications of communications and electronics technologies, and aims at continuing and accelerating the momentum of researching in electronics and telecommunications areas. ICCE 2010 will also provide the best and most current tutorials, research results, industry-oriented technical contents, thereby facilitating scientific idea exchange and the identification and definition of future trends and directions on these fields.

  • 2008 Second International Conference on Communications and Electronics (ICCE)

    The Second International Conference on Communications and Electronics (ICCE 2008) is devoted to the research, development, and application of communications and electronics technologies, and aims at continuing and accelerating the momentum of researching in telecommunication areas.

  • 2006 First International Conference on Communications and Electronics (ICCE)


2018 International Symposium on Consumer Technologies (ISCT)

IEEE ISCT International Symposium on Consumer Technologies (former International Symposium on Consumer Electronics) is the established forum for innovative research in all technology areas of consumer electronics. The theme of ISCT 2018 is “Consumer Technologies in 10 years”.Paper contributions are sought in but are not limited to following areas:Internet of Things and Internet of Everywhere (IoT)Consumer Healthcare Systems (CHS)Energy Management of CE Hardware and Software Systems (EMC)Application-Specific CE for Smart Cities (SMC)Artificial Intelligence in Consumer Technologies (AIC)Consumer Technologies Quality and Testing (CTQ)Telecom, RF, Wireless, and Network Technologies (WNT)AV Systems, Image and Video, and Cameras and Acquisition (AVS)CE Sensors and MEMS (CSM)Smartphone and Mobile Device Technologies (MDT)Entertainment, Gaming, and Virtual and Augmented Reality (EGV)Other Technologies Related with CE (MIS)Education in Consumer Technologies Area (EDU)


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Periodicals related to Speech Recognition

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Aerospace and Electronic Systems Magazine, IEEE

The IEEE Aerospace and Electronic Systems Magazine publishes articles concerned with the various aspects of systems for space, air, ocean, or ground environments.


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; ...


Biomedical Engineering, IEEE Transactions on

Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.


Consumer Electronics, IEEE Transactions on

The design and manufacture of consumer electronics products, components, and related activities, particularly those used for entertainment, leisure, and educational purposes


Neural Networks, IEEE Transactions on

Devoted to the science and technology of neural networks, which disclose significant technical knowledge, exploratory developments, and applications of neural networks from biology to software to hardware. Emphasis is on artificial neural networks.


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Most published Xplore authors for Speech Recognition

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Xplore Articles related to Speech Recognition

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Audio-visual automatic speech recognition and related bimodal speech technologies: A review of the state-of-the-art and open problems

[{u'author_order': 1, u'affiliation': u'Institute of Informatics and Telecommunications, National Centre for Scientific Research \xbfDemokritos\xbf, GR-15310 Athens, Greece', u'full_name': u'Gerasimos Potamianos'}] 2009 IEEE Workshop on Automatic Speech Recognition & Understanding, 2009

Summary form only given. The presentation will provide an overview of the main research achievements and the state-of-the-art in the area of audiovisual speech processing, mainly focusing in the area of audio-visual automatic speech recognition. The topic has been of interest in the speech research community due to the potential of increased robustness to acoustic noise that the visual modality ...


Acoustic modelling for speech recognition: Hidden Markov models and beyond?

[{u'author_order': 1, u'affiliation': u'Department of Engineering, University of Cambridge, UK', u'full_name': u'M.J.F. Gales'}] 2009 IEEE Workshop on Automatic Speech Recognition & Understanding, 2009

Hidden Markov models (HMMs) are still the dominant form of acoustic model used in automatic speech recognition (ASR) systems. However over the years the form, and training, of the HMM for ASR have been extended and modified, so that the current forms used in state-of-the-art speech recognition systems are very different to those originally proposed thirty years ago. This talk ...


A fifteen channels real time speech recognition board for computer telephony applications

[{u'author_order': 1, u'affiliation': u'Dept. of Speech Recognition, NMS-Europe, Clamart, France', u'full_name': u'C. Gerard'}, {u'author_order': 2, u'full_name': u'A. Ouahabi'}] IEEE Instrumentation and Measurement Technology Conference Sensing, Processing, Networking. IMTC Proceedings, 1997

In the field of automatic speech recognition, telephony providers need high recognition scores, fast download model and large vocabulary capacities. These demands often lead to expensive solutions. The board described offers a low cost configuration since it can handles a large amount of recognition parts. In this paper, two firmware architectures are presented and discussed.


Pronunciation modelling for conversational speech recognition: a status report from WS97

[{u'author_order': 1, u'affiliation': u'Center for Language & Speech Process., Johns Hopkins Univ., Baltimore, MD, USA', u'full_name': u'B. Byrne'}, {u'author_order': 2, u'full_name': u'M. Finke'}, {u'author_order': 3, u'full_name': u'S. Khudanpur'}, {u'author_order': 4, u'full_name': u'J. McDonough'}, {u'author_order': 5, u'full_name': u'H. Nock'}, {u'author_order': 6, u'full_name': u'M. Riley'}, {u'author_order': 7, u'full_name': u'M. Saraclar'}, {u'author_order': 8, u'full_name': u'C. Wooters'}, {u'author_order': 9, u'full_name': u'G. Zavaliagkos'}] 1997 IEEE Workshop on Automatic Speech Recognition and Understanding Proceedings, 1997

Accurately modelling of pronunciation variability in conversational speech is an important component for automatic speech recognition. We describe some of the projects undertaken in this direction at WS97 [the Fifth LVCSR (large- vocabulary conversational speech recognition) Summer Workshop], held at Johns Hopkins University, Baltimore, in July-August 1997. We first illustrate a use of hand-labelled phonetic transcriptions of a portion of ...


Combined optimisation of baseforms and model parameters in speech recognition based on acoustic subword units

[{u'author_order': 1, u'affiliation': u'Dept. of Telecommun., Norwegian Univ. of Sci. & Technol., Norway', u'full_name': u'T. Holter'}, {u'author_order': 2, u'full_name': u'T. Svendsen'}] 1997 IEEE Workshop on Automatic Speech Recognition and Understanding Proceedings, 1997

A major challenge in speech recognition is creating a lexicon which is robust to inter and intra speaker variations. This is even more so in speech recognisers based on non linguistic units, e.g., acoustic subword units (ASWUs), since no standard pronunciation dictionaries are available. Thus the baseforms describing the vocabulary words in terms of the recognition units need to be ...


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Educational Resources on Speech Recognition

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eLearning

No eLearning Articles are currently tagged "Speech Recognition"

IEEE-USA E-Books

  • Speech Recognition Algorithms based on Weighted Finite-State Transducers

    This book introduces the theory, algorithms, and implementation techniques for efficient decoding in speech recognition mainly focusing on the Weighted Finite-State Transducer (WFST) approach. The decoding process for speech recognition is viewed as a search problem whose goal is to find a sequence of words that best matches an input speech signal. Since this process becomes computationally more expensive as the system vocabulary size increases, research has long been devoted to reducing the computational cost. Recently, the WFST approach has become an important state-of-the-art speech recognition technology, because it offers improved decoding speed with fewer recognition errors compared with conventional methods. However, it is not easy to understand all the algorithms used in this framework, and they are still in a black box for many people. In this book, we review the WFST approach and aim to provide comprehensive interpretations of WFST operations and decoding algorithms to help anyone who wants to understand, develop, and study WFST- based speech recognizers. We also mention recent advances in this framework and its applications to spoken language processing. Table of Contents: Introduction / Brief Overview of Speech Recognition / Introduction to Weighted Finite-State Transducers / Speech Recognition by Weighted Finite-State Transducers / Dynamic Decoders with On-the-fly WFST Operations / Summary and Perspective

  • Discriminative Learning for Speech Recognition

    In this book, we introduce the background and mainstream methods of probabilistic modeling and discriminative parameter optimization for speech recognition. The specific models treated in depth include the widely used exponential-family distributions and the hidden Markov model. A detailed study is presented on unifying the common objective functions for discriminative learning in speech recognition, namely maximum mutual information (MMI), minimum classification error, and minimum phone/word error. The unification is presented, with rigorous mathematical analysis, in a common rational-function form. This common form enables the use of the growth transformation (or extended Baum–Welch) optimization framework in discriminative learning of model parameters. In addition to all the necessary introduction of the background and tutorial material on the subject, we also included technical details on the derivation of the parameter optimization formulas for exponential-family distributions, discrete hidden Markov models (HMMs), and continuous-density HMMs in discriminative learning. Selected experimental results obtained by the authors in firsthand are presented to show that discriminative learning can lead to superior speech recognition performance over conventional parameter learning. Details on major algorithmic implementation issues with practical significance are provided to enable the practitioners to directly reproduce the theory in the earlier part of the book into engineering practice. Table of Contents: Introduction and Background / Statistical Speech Recognition: A Tutorial / Discriminative Learning: A Unified Objective Function / Discriminative Learning Algorithm for Exponential-Family Distributions / Discriminative Learning Algorithm for Hidden Markov Model / Practical Implementation of Discriminative Learning / Selected Experimental Results / Epilogue / Major Symbols Used in the Book and Their Descriptions / Mathematical Notation / Bibliography

  • Application of Hidden Markov Models in Speech Recognition

    Hidden Markov Models (HMMs) provide a simple and effective framework for modelling time-varying spectral vector sequences. As a consequence, almost all present day large vocabulary continuous speech recognition (LVCSR) systems are based on HMMs. Whereas the basic principles underlying HMM-based LVCSR are rather straightforward, the approximations and simplifying assumptions involved in a direct implementation of these principles would result in a system which has poor accuracy and unacceptable sensitivity to changes in operating environment. Thus, the practical application of HMMs in modern systems involves considerable sophistication. The Application of Hidden Markov Models in Speech Recognition presents the core architecture of a HMM-based LVCSR system and proceeds to describe the various refinements which are needed to achieve state-of-the-art performance. These refinements include feature projection, improved covariance modelling, discriminative parameter estimation, adaptation and normalisation, noise compensation and multi-pass system combination. It concludes with a case study of LVCSR for Broadcast News and Conversation transcription in order to illustrate the techniques described. The Application of Hidden Markov Models in Speech Recognition is an invaluable resource for anybody with an interest in speech recognition technology.

  • The Basics of Automatic Speech Recognition

    This chapter contains sections titled:IntroductionSpeech Recognition Viewed as Bayes ClassificationHidden Markov ModelsHMM‐Based Speech RecognitionReferences

  • Computational Auditory Scene Analysis and Automatic Speech Recognition

    This chapter contains sections titled:IntroductionAuditory Scene AnalysisComputational Auditory Scene AnalysisCASA StrategiesIntegrating CASA with ASRConcluding RemarksAcknowledgmentReferences

  • Reverberant Speech Recognition

    This chapter contains sections titled:IntroductionThe Effect of ReverberationApproaches to Reverberant Speech RecognitionFeature Domain Model of the Acoustic Impulse ResponseBayesian Feature EnhancementExperimental ResultsConclusionsAcknowledgmentReferences

  • Factorial Models for Noise Robust Speech Recognition

    This chapter contains sections titled:IntroductionThe Model‐Based ApproachSignal Feature DomainsInteraction ModelsInference MethodsEfficient Likelihood Evaluation in Factorial ModelsCurrent DirectionsReferences

  • The Problem of Robustness in Automatic Speech Recognition

    This chapter contains sections titled:Errors in Bayes ClassificationBayes Classification and ASRExternal Influences on Speech RecordingsThe Effect of External Influences on RecognitionImproving Recognition under Adverse ConditionsReferences

  • Acoustic Model Training for Robust Speech Recognition

    This chapter contains sections titled:IntroductionTraditional Training Methods for Robust Speech RecognitionA Brief Overview of Speaker Adaptive TrainingFeature‐Space Noise Adaptive TrainingModel‐Space Noise Adaptive TrainingNoise Adaptive Training using VTS AdaptationDiscussionConclusionReferences

  • Introduction

    This chapter contains sections titled:Scope of the BookOutlineNotation



Standards related to Speech Recognition

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Jobs related to Speech Recognition

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