Hierarchical Neural Network Structures For Phoneme Recognition
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Hierarchical Neural Network Structures For Phoneme Recognition
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Author : Daniel Vasquez
language : en
Publisher: Springer
Release Date : 2012-10-18
Hierarchical Neural Network Structures For Phoneme Recognition written by Daniel Vasquez and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-10-18 with Technology & Engineering categories.
In this book, hierarchical structures based on neural networks are investigated for automatic speech recognition. These structures are mainly evaluated within the phoneme recognition task under the Hybrid Hidden Markov Model/Artificial Neural Network (HMM/ANN) paradigm. The baseline hierarchical scheme consists of two levels each which is based on a Multilayered Perceptron (MLP). Additionally, the output of the first level is used as an input for the second level. This system can be substantially speeded up by removing the redundant information contained at the output of the first level.
Hierarchical Neural Network Structures For Phoneme Recognition
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Author : Daniel Vasquez
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-10-18
Hierarchical Neural Network Structures For Phoneme Recognition written by Daniel Vasquez and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-10-18 with Technology & Engineering categories.
In this book, hierarchical structures based on neural networks are investigated for automatic speech recognition. These structures are mainly evaluated within the phoneme recognition task under the Hybrid Hidden Markov Model/Artificial Neural Network (HMM/ANN) paradigm. The baseline hierarchical scheme consists of two levels each which is based on a Multilayered Perceptron (MLP). Additionally, the output of the first level is used as an input for the second level. This system can be substantially speeded up by removing the redundant information contained at the output of the first level.
Hierarchical Neural Network Structures For Phoneme Recognition
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Author : Daniel Vasquez
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-10-17
Hierarchical Neural Network Structures For Phoneme Recognition written by Daniel Vasquez and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-10-17 with Technology & Engineering categories.
In this book, hierarchical structures based on neural networks are investigated for automatic speech recognition. These structures are mainly evaluated within the phoneme recognition task under the Hybrid Hidden Markov Model/Artificial Neural Network (HMM/ANN) paradigm. The baseline hierarchical scheme consists of two levels each which is based on a Multilayered Perceptron (MLP). Additionally, the output of the first level is used as an input for the second level. This system can be substantially speeded up by removing the redundant information contained at the output of the first level.
Artificial Intelligence And Speech Technology
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Author : Amita Dev
language : en
Publisher: Springer Nature
Release Date : 2022-01-28
Artificial Intelligence And Speech Technology written by Amita Dev and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-28 with Computers categories.
This volume constitutes selected papers presented at the Third International Conference on Artificial Intelligence and Speech Technology, AIST 2021, held in Delhi, India, in November 2021. The 36 full papers and 18 short papers presented were thoroughly reviewed and selected from the 178 submissions. They provide a discussion on application of Artificial Intelligence tools in speech analysis, representation and models, spoken language recognition and understanding, affective speech recognition, interpretation and synthesis, speech interface design and human factors engineering, speech emotion recognition technologies, audio-visual speech processing and several others.
Supervised Sequence Labelling With Recurrent Neural Networks
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Author : Alex Graves
language : en
Publisher: Springer
Release Date : 2012-02-06
Supervised Sequence Labelling With Recurrent Neural Networks written by Alex Graves and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-02-06 with Technology & Engineering categories.
Supervised sequence labelling is a vital area of machine learning, encompassing tasks such as speech, handwriting and gesture recognition, protein secondary structure prediction and part-of-speech tagging. Recurrent neural networks are powerful sequence learning tools—robust to input noise and distortion, able to exploit long-range contextual information—that would seem ideally suited to such problems. However their role in large-scale sequence labelling systems has so far been auxiliary. The goal of this book is a complete framework for classifying and transcribing sequential data with recurrent neural networks only. Three main innovations are introduced in order to realise this goal. Firstly, the connectionist temporal classification output layer allows the framework to be trained with unsegmented target sequences, such as phoneme-level speech transcriptions; this is in contrast to previous connectionist approaches, which were dependent on error-prone prior segmentation. Secondly, multidimensional recurrent neural networks extend the framework in a natural way to data with more than one spatio-temporal dimension, such as images and videos. Thirdly, the use of hierarchical subsampling makes it feasible to apply the framework to very large or high resolution sequences, such as raw audio or video. Experimental validation is provided by state-of-the-art results in speech and handwriting recognition.
Advances In Nonlinear Speech Processing
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Author : Jordi Sole-Casals
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-02-18
Advances In Nonlinear Speech Processing written by Jordi Sole-Casals and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-02-18 with Computers categories.
This volume contains the proceedings of NOLISP 2009, an ISCA Tutorial and Workshop on Non-Linear Speech Processing held at the University of Vic (- talonia, Spain) during June 25-27, 2009. NOLISP2009wasprecededbythreeeditionsofthisbiannualeventheld2003 in Le Croisic (France), 2005 in Barcelona, and 2007 in Paris. The main idea of NOLISP workshops is to present and discuss new ideas, techniques and results related to alternative approaches in speech processing that may depart from the mainstream. In order to work at the front-end of the subject area, the following domains of interest have been de?ned for NOLISP 2009: 1. Non-linear approximation and estimation 2. Non-linear oscillators and predictors 3. Higher-order statistics 4. Independent component analysis 5. Nearest neighbors 6. Neural networks 7. Decision trees 8. Non-parametric models 9. Dynamics for non-linear systems 10. Fractal methods 11. Chaos modeling 12. Non-linear di?erential equations The initiative to organize NOLISP 2009 at the University of Vic (UVic) came from the UVic Research Group on Signal Processing and was supported by the Hardware-Software Research Group. We would like to acknowledge the ?nancial support obtained from the M- istry of Science and Innovation of Spain (MICINN), University of Vic, ISCA, and EURASIP. All contributions to this volume are original. They were subject to a doub- blind refereeing procedure before their acceptance for the workshop and were revised after being presented at NOLISP 2009.
Deep Learning Classifiers With Memristive Networks
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Author : Alex Pappachen James
language : en
Publisher: Springer
Release Date : 2019-04-08
Deep Learning Classifiers With Memristive Networks written by Alex Pappachen James and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-08 with Technology & Engineering categories.
This book introduces readers to the fundamentals of deep neural network architectures, with a special emphasis on memristor circuits and systems. At first, the book offers an overview of neuro-memristive systems, including memristor devices, models, and theory, as well as an introduction to deep learning neural networks such as multi-layer networks, convolution neural networks, hierarchical temporal memory, and long short term memories, and deep neuro-fuzzy networks. It then focuses on the design of these neural networks using memristor crossbar architectures in detail. The book integrates the theory with various applications of neuro-memristive circuits and systems. It provides an introductory tutorial on a range of issues in the design, evaluation techniques, and implementations of different deep neural network architectures with memristors.
Hierarchical Neural Networks For Image Interpretation
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Author : Sven Behnke
language : en
Publisher: Springer
Release Date : 2003-11-18
Hierarchical Neural Networks For Image Interpretation written by Sven Behnke and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-11-18 with Computers categories.
Human performance in visual perception by far exceeds the performance of contemporary computer vision systems. While humans are able to perceive their environment almost instantly and reliably under a wide range of conditions, computer vision systems work well only under controlled conditions in limited domains. This book sets out to reproduce the robustness and speed of human perception by proposing a hierarchical neural network architecture for iterative image interpretation. The proposed architecture can be trained using unsupervised and supervised learning techniques. Applications of the proposed architecture are illustrated using small networks. Furthermore, several larger networks were trained to perform various nontrivial computer vision tasks.
New Era For Robust Speech Recognition
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Author : Shinji Watanabe
language : en
Publisher: Springer
Release Date : 2017-10-30
New Era For Robust Speech Recognition written by Shinji Watanabe and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-10-30 with Computers categories.
This book covers the state-of-the-art in deep neural-network-based methods for noise robustness in distant speech recognition applications. It provides insights and detailed descriptions of some of the new concepts and key technologies in the field, including novel architectures for speech enhancement, microphone arrays, robust features, acoustic model adaptation, training data augmentation, and training criteria. The contributed chapters also include descriptions of real-world applications, benchmark tools and datasets widely used in the field. This book is intended for researchers and practitioners working in the field of speech processing and recognition who are interested in the latest deep learning techniques for noise robustness. It will also be of interest to graduate students in electrical engineering or computer science, who will find it a useful guide to this field of research.
Advances In Pattern Recognition Systems Using Neural Network Technologies
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Author : Patrick S P Wang
language : en
Publisher: World Scientific
Release Date : 1994-01-01
Advances In Pattern Recognition Systems Using Neural Network Technologies written by Patrick S P Wang and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 1994-01-01 with categories.
Contents:A Connectionist Approach to Speech Recognition (Y Bengio)Signature Verification Using a “Siamese” Time Delay Neural Network (J Bromley et al.)Boosting Performance in Neural Networks (H Drucker et al.)An Integrated Architecture for Recognition of Totally Unconstrained Handwritten Numerals (A Gupta et al.)Time-Warping Network: A Neural Approach to Hidden Markov Model Based Speech Recognition (E Levin et al.)Computing Optical Flow with a Recurrent Neural Network (H Li & J Wang)Integrated Segmentation and Recognition through Exhaustive Scans or Learned Saccadic Jumps (G L Martin et al.)Experimental Comparison of the Effect of Order in Recurrent Neural Networks (C B Miller & C L Giles)Adaptive Classification by Neural Net Based Prototype Populations (K Peleg & U Ben-Hanan)A Neural System for the Recognition of Partially Occluded Objects in Cluttered Scenes: A Pilot Study (L Wiskott & C von der Malsburg)and other papers Readership: Computer scientists and engineers.