Download or read book Discriminative Complexity Control and Linear Projections for Large Vocabulary Speech Recognition written by Xunying Liu and published by . This book was released on 2006 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Discriminative Training of Multiple Subspace Projections for Large Vocabulary Speech Recognition written by X. Liu and published by . This book was released on 2004 with total page 22 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Discriminative Training of Multiple Suspace Projections for Large Vocabulary Speech Recognition written by X. Liu and published by . This book was released on 2004 with total page 22 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book The Application of Hidden Markov Models in Speech Recognition written by Mark Gales and published by Now Publishers Inc. This book was released on 2008 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

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 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 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 Large Margin Structured Prediction Extensions of Neural Networks for Automatic Speech Recognition written by Suman Ravuri and published by . This book was released on 2015 with total page 98 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural networks, especially those with more than one hidden layer, have re-emerged in Automatic Speech Recognition (ASR) systems as replacements to emission models based on Gaussian Mixture Models (GMMs). While the use of these so-called Deep Neural Networks (DNNs) has enjoyed widespread success due to improvements in recognition results, the exact source of better recognition accuracy is not entirely understood. Using a bootstrap resampling framework that generates synthetic test set data satisfying conditional independence assumptions of the model while still using real observations, I show that DNNs used for both feature generation and hybrid acoustic modeling help compensate for incorrect conditional independence assumptions and help fix poor phone duration estimates of the hidden Markov Model (HMM). Despite these improvements, the large increase in word error rates for DNN-HMM systems on real data compared to synthetic data suggests that one can improve recognition performance by modifying the training criterion. Since neural networks are log-linear at the output layer, I propose using sequences of last hidden layers as input to a log-linear model, and training that model with large-margin criteria. These Structured Support Vector Machine (SVM) approaches allow us to more directly minimize errors relevant to automatic speech recognition, and provide some guarantees on test set error. First, I show how one can generate better features by combining a neural network with a hidden Markov Support Vector Machine (HMSVM). Then, I propose a hybrid DNN-Structured SVM acoustic model and an online training algorithm that iteratively updates alignments for faster convergence. Training of this model falls under a class of approaches known as sequence-discriminative training, which are used to train state-of-the-art systems. This DNN-latent Structured SVM model beats alternative methods to sequence-discriminative training by 1.0% absolute, while needing 33-66% fewer utterances to converge. Finally, I analyze the Structured SVM approach to sequence-discriminative training and compare it to standard methods. I show how the loss function for boosted Maximum Mutual Information is an upper bound of the hinge loss for the Structured SVM, and how such a relaxation precludes the use of aggressive boosting parameters needed for better results. Finally, I analyze four of the most popular sequence-discriminative training criteria – Maximum Mutual Information, boosted Maximum Mutual Information, Minimum Phone Error, and state-level Minimum Bayes Risk – and the latent Structured SVM using the bootstrap resampling framework, and compare how different sequence-discriminative training criteria compensate for data/model mismatch. Structured SVM models perform better for real rather than synthetic data, likely because the model makes fewer distributional assumptions about the underlying data.

Download or read book High Accuracy Large Vocabulary Speech Recognition Using Mixture Tying and Consistency Modeling written by and published by . This book was released on 1994 with total page 7 pages. Available in PDF, EPUB and Kindle. Book excerpt: Improved acoustic modeling can significantly decrease the error rate in large-vocabulary speech recognition. Our approach to the problem is twofold. We first propose a scheme that optimizes the degree of mixture tying for a given amount of training data and computational resources. Experimental results on the Wall Street Journal (WSJ) Corpus show that this new form of output distribution achieves a 25% reduction in error rate over typical tied- mixture systems. We then show that an additional improvement can be achieved by modeling local time correlation with linear discriminant features.

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 Distant Speech Recognition written by Matthias Woelfel and published by John Wiley & Sons. This book was released on 2009-04-20 with total page 600 pages. Available in PDF, EPUB and Kindle. Book excerpt: A complete overview of distant automatic speech recognition The performance of conventional Automatic Speech Recognition (ASR) systems degrades dramatically as soon as the microphone is moved away from the mouth of the speaker. This is due to a broad variety of effects such as background noise, overlapping speech from other speakers, and reverberation. While traditional ASR systems underperform for speech captured with far-field sensors, there are a number of novel techniques within the recognition system as well as techniques developed in other areas of signal processing that can mitigate the deleterious effects of noise and reverberation, as well as separating speech from overlapping speakers. Distant Speech Recognitionpresents a contemporary and comprehensive description of both theoretic abstraction and practical issues inherent in the distant ASR problem. Key Features: Covers the entire topic of distant ASR and offers practical solutions to overcome the problems related to it Provides documentation and sample scripts to enable readers to construct state-of-the-art distant speech recognition systems Gives relevant background information in acoustics and filter techniques, Explains the extraction and enhancement of classification relevant speech features Describes maximum likelihood as well as discriminative parameter estimation, and maximum likelihood normalization techniques Discusses the use of multi-microphone configurations for speaker tracking and channel combination Presents several applications of the methods and technologies described in this book Accompanying website with open source software and tools to construct state-of-the-art distant speech recognition systems This reference will be an invaluable resource for researchers, developers, engineers and other professionals, as well as advanced students in speech technology, signal processing, acoustics, statistics and artificial intelligence fields.

Download or read book Bayesian Discriminative Speaker Adaptation for Large Vocabulary Continuous Speech Recognition written by 黃志賢 and published by . This book was released on 2006 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Speech Recognition and Understanding written by Pietro Laface and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 557 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book collects the contributions to the NATO Advanced Study Institute on "Speech Recognition and Understanding: Recent Advances, Trends and Applications", held in Cetraro, Italy, during the first two weeks of July 1990. This Institute focused on three topics that are considered of particular interest and rich of i'p.novation by researchers in the fields of speech recognition and understanding: Advances in Hidden Markov modeling, connectionist approaches to speech and language modeling, and linguistic processing including language and dialogue modeling. The purpose of any ASI is that of encouraging scientific communications between researchers of NATO countries through advanced tutorials and presentations: excellent tutorials were offered by invited speakers that present in this book 15 papers which sum marize or detail the topics covered in their lectures. The lectures were complemented by discussions, panel sections and by the presentation of related works carried on by some of the attending researchers: these presentations have been collected in 42 short contributions to the Proceedings. This volume, that the reader can find useful for an overview, although incomplete, of the state of the art in speech understanding, is divided into 6 Parts.

Download or read book Index to Theses with Abstracts Accepted for Higher Degrees by the Universities of Great Britain and Ireland and the Council for National Academic Awards written by and published by . This book was released on 2006 with total page 648 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Large margin Gaussian Mixture Modeling for Automatic Speech Recognition written by Hung-An Chang (Ph. D.) and published by . This book was released on 2008 with total page 103 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discriminative training for acoustic models has been widely studied to improve the performance of automatic speech recognition systems. To enhance the generalization ability of discriminatively trained models, a large-margin training framework has recently been proposed. This work investigates large-margin training in detail, integrates the training with more flexible classifier structures such as hierarchical classifiers and committee-based classifiers, and compares the performance of the proposed modeling scheme with existing discriminative methods such as minimum classification error (MCE) training. Experiments are performed on a standard phonetic classification task and a large vocabulary speech recognition (LVCSR) task. In the phonetic classification experiments, the proposed modeling scheme yields about 1.5% absolute error reduction over the current state of the art. In the LVCSR experiments on the MIT lecture corpus, the large-margin model has about 6.0% absolute word error rate reduction over the baseline model and about 0.6% absolute error rate reduction over the MCE model.

Download or read book Progressive Search Algorithms for Large Vocabulary Speech Recognition written by and published by . This book was released on 1993 with total page 5 pages. Available in PDF, EPUB and Kindle. Book excerpt: The authors describe a technique they call "Progressive Search," which is useful for developing and implementing speech recognition systems with high computational requirements. The scheme iteratively uses more and more complex recognition schemes, where each iteration constrains the search space of the next. An algorithm, the "Forward-Backward Word-Life Algorithm," is described. It can generate a word lattice in a progressive search that would be used as a language model embedded in a succeeding recognition pass to reduce computation requirements. They show that speed-ups of more than an order of magnitude are achievable with only minor costs in accuracy.

Download or read book A Log linear Discriminative Modeling Framework for Speech Recognition written by Georg Heigold and published by . This book was released on 2010 with total page 191 pages. Available in PDF, EPUB and Kindle. Book excerpt: