Download or read book Discriminative Learning for Speech Recognition written by Xiadong He and published by Springer Nature. This book was released on 2022-06-01 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Automatic Speech Recognition written by Dong Yu and published by Springer. This book was released on 2014-11-11 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive overview of the recent advancement in the field of automatic speech recognition with a focus on deep learning models including deep neural networks and many of their variants. This is the first automatic speech recognition book dedicated to the deep learning approach. In addition to the rigorous mathematical treatment of the subject, the book also presents insights and theoretical foundation of a series of highly successful deep learning models.

Download or read book Generalized Discriminative Training for Speech Recognition written by Wend-Huu Roger Hsiao and published by . This book was released on 2012 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Discriminative Training for Speech Recognition written by Yoh'ichi Tohkura and published by . This book was released on 1992 with total page 119 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Robust Automatic Speech Recognition written by Jinyu Li and published by Academic Press. This book was released on 2015-10-30 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robust Automatic Speech Recognition: A Bridge to Practical Applications establishes a solid foundation for automatic speech recognition that is robust against acoustic environmental distortion. It provides a thorough overview of classical and modern noise-and reverberation robust techniques that have been developed over the past thirty years, with an emphasis on practical methods that have been proven to be successful and which are likely to be further developed for future applications.The strengths and weaknesses of robustness-enhancing speech recognition techniques are carefully analyzed. The book covers noise-robust techniques designed for acoustic models which are based on both Gaussian mixture models and deep neural networks. In addition, a guide to selecting the best methods for practical applications is provided.The reader will: Gain a unified, deep and systematic understanding of the state-of-the-art technologies for robust speech recognition Learn the links and relationship between alternative technologies for robust speech recognition Be able to use the technology analysis and categorization detailed in the book to guide future technology development Be able to develop new noise-robust methods in the current era of deep learning for acoustic modeling in speech recognition The first book that provides a comprehensive review on noise and reverberation robust speech recognition methods in the era of deep neural networks Connects robust speech recognition techniques to machine learning paradigms with rigorous mathematical treatment Provides elegant and structural ways to categorize and analyze noise-robust speech recognition techniques Written by leading researchers who have been actively working on the subject matter in both industrial and academic organizations for many years

Download or read book New Era for Robust Speech Recognition written by Shinji Watanabe and published by Springer. This book was released on 2017-10-30 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Automatic Speech and Speaker Recognition written by Joseph Keshet and published by John Wiley & Sons. This book was released on 2009-04-27 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses large margin and kernel methods for speech and speaker recognition Speech and Speaker Recognition: Large Margin and Kernel Methods is a collation of research in the recent advances in large margin and kernel methods, as applied to the field of speech and speaker recognition. It presents theoretical and practical foundations of these methods, from support vector machines to large margin methods for structured learning. It also provides examples of large margin based acoustic modelling for continuous speech recognizers, where the grounds for practical large margin sequence learning are set. Large margin methods for discriminative language modelling and text independent speaker verification are also addressed in this book. Key Features: Provides an up-to-date snapshot of the current state of research in this field Covers important aspects of extending the binary support vector machine to speech and speaker recognition applications Discusses large margin and kernel method algorithms for sequence prediction required for acoustic modeling Reviews past and present work on discriminative training of language models, and describes different large margin algorithms for the application of part-of-speech tagging Surveys recent work on the use of kernel approaches to text-independent speaker verification, and introduces the main concepts and algorithms Surveys recent work on kernel approaches to learning a similarity matrix from data This book will be of interest to researchers, practitioners, engineers, and scientists in speech processing and machine learning fields.

Download or read book Discriminative Training for Speech Recognition written by Erik McDermott and published by . This book was released on 1997 with total page 195 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Discriminative Training for Continuous Speech Recognition written by Wolfgang Reichl and published by . This book was released on 1996 with total page 8 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Discriminative Training for Large Vocabulary Speech Recognition written by Daniel Povey and published by . This book was released on 2005 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Discriminative Manifold Learning for Automatic Speech Recognition written by Vikrant Tomar and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "Manifold learning techniques have received a lot of attention in recent literature. The underlying assumption of these techniques is that the high-dimensional data can be considered as a set of geometrically related points lying on or close to the surface of a smooth low-dimensional manifold embedded in the ambient space. These techniques have been used in a wide variety of application domains, such as face recognition, speaker and speech recognition. In automatic speech recognition (ASR), previous studies on this topic have primarily focused on unsupervised manifold learning techniques for dimensionality reducing feature space transformations. The goal of these techniques is to preserve the underlying manifold based geometrical relationship existing in the speech data during the transformation. However, these techniques fail to exploit the discriminative structure between the classes of speech sounds. The work in this thesis has investigated incorporating inter-class discrimination into manifold learning techniques. The contributions of this thesis work can be divided in two major categories. The first is the discriminative manifold learning (DML) techniques for dimensionality reducing feature space transformation. The second is to use the DML based constraints to regularize the training of deep neural networks (DNN). The first contribution of this thesis is a framework for DML based feature space transformations for ASR. These techniques attempt to preserve the local manifold based nonlinear relationships between feature vectors while maximizing a criterion related to separating speech classes. Two different techniques are proposed. The first is the locality preserving discriminant analysis (LPDA). In LPDA, the manifold domain relationships between feature vectors are characterized by a Euclidean distance based kernel. The second technique is the correlation preserving discriminant analysis (CPDA), which uses a cosine-correlational kernel. The LPDA and CPDA techniques are compared to two well known approaches for dimensionality reducing transformations, linear discriminant analysis (LDA) and locality preserving projection (LPP), on two separate tasks involving noise corrupted utterances of both connected digits and read newspaper text. The proposed approaches are found to provide up to 30% reductions in word error rates (WER) with respect to LDA and LPP. The second major contribution of this thesis is to apply the DML based constraints to optimize the training of DNNs for ASR. DNNs have been successfully applied to a variety of ASR tasks, both in discriminative feature extraction and hybrid acoustic modeling scenarios. Despite the rapid progress in DNN research, a number of challenges remain in training DNNs. In this part of the thesis, a manifold regularized deep neural network (MRDNN) training approach is proposed that constrains the network learning to preserve the underlying manifold based relationships between speech feature vectors. This is achieved by incorporating manifold based locality preserving constraints in the objective criterion of the network. Empirical evidence is provided to demonstrate that training a network with manifold constraints strengthens the learning of manifold based neighborhood preservation and preserves structural compactness in the hidden layers of the network. The ASR WER obtained using these networks is evaluated on a connected digits speech in noise task and a read news speech in noise task. Compared to DNNs trained without manifold constraints, the MRDNNs provides 10 to 38.64% reductions in ASR WERs. " --

Download or read book Pattern Recognition in Speech and Language Processing written by Wu Chou and published by CRC Press. This book was released on 2003-02-26 with total page 413 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the last 20 years, approaches to designing speech and language processing algorithms have moved from methods based on linguistics and speech science to data-driven pattern recognition techniques. These techniques have been the focus of intense, fast-moving research and have contributed to significant advances in this field. Pattern Reco

Download or read book Optimizing the Performance of GPD based Discriminative Training in Speech Recognition written by and published by . This book was released on with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: HKUST Call Number: Thesis ELEC 2000 LamWB.

Download or read book A Discriminative Training Algorithm and Its Implementation to Automatic Speech Recognition written by Assaf Ben-Yishai and published by . This book was released on 2001 with total page 60 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Discriminative Training and Acoustic Modeling for Automatic Speech Recognition written by Wolfgang Macherey and published by . This book was released on 2010 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Objective driven Discriminative Training and Adaptation Based on an MCE Criterion for Speech Recognition and Detection written by Sung-Hwan Shin and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Acoustic modeling in state-of-the-art speech recognition systems is commonly based on discriminative criteria. Different from the paradigm of the conventional distribution estimation such as maximum a posteriori (MAP) and maximum likelihood (ML), the most popular discriminative criteria such as MCE and MPE aim at direct minimization of the empirical error rate. As recent ASR applications become diverse, it has been increasingly recognized that realistic applications often require a model that can be optimized for a task-specific goal or a particular scenario beyond the general purposes of the current discriminative criteria. These specific requirements cannot be directly handled by the current discriminative criteria since the objective of the criteria is to minimize the overall empirical error rate. :In this thesis, we propose novel objective-driven discriminative training and adaptation frameworks, which are generalized from the minimum classification error (MCE) criterion, for various tasks and scenarios of speech recognition and detection. The proposed frameworks are constructed to formulate new discriminative criteria which satisfy various requirements of the recent ASR applications. In this thesis, each objective required by an application or a developer is directly embedded into the learning criterion. Then, the objective-driven discriminative criterion is used to optimize an acoustic model in order to achieve the required objective. :Three task-specific requirements that the recent ASR applications often require in practice are mainly taken into account in developing the objective-driven discriminative criteria. First, an issue of individual error minimization of speech recognition is addressed and we propose a direct minimization algorithm for each error type of speech recognition. Second, a rapid adaptation scenario is embedded into formulating discriminative linear transforms under the MCE criterion. A regularized MCE criterion is proposed to efficiently improve the generalization capability of the MCE estimate in a rapid adaptation scenario. Finally, the particular operating scenario that requires a system model optimized at a given specific operating point is discussed over the conventional receiver operating characteristic (ROC) optimization. A constrained discriminative training algorithm which can directly optimize a system model for any particular operating need is proposed. For each of the developed algorithms, we provide an analytical solution and an appropriate optimization procedure.

Download or read book Discriminative Models for Speech Recognition written by Anton Ragni and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: