Download or read book Acoustic Models for Posterior Features in Speech Recognition written by Guillermo Aradilla and published by . This book was released on 2008 with total page 136 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 Robust Speech Recognition of Uncertain or Missing Data written by Dorothea Kolossa and published by Springer Science & Business Media. This book was released on 2011-07-14 with total page 387 pages. Available in PDF, EPUB and Kindle. Book excerpt: Automatic speech recognition suffers from a lack of robustness with respect to noise, reverberation and interfering speech. The growing field of speech recognition in the presence of missing or uncertain input data seeks to ameliorate those problems by using not only a preprocessed speech signal but also an estimate of its reliability to selectively focus on those segments and features that are most reliable for recognition. This book presents the state of the art in recognition in the presence of uncertainty, offering examples that utilize uncertainty information for noise robustness, reverberation robustness, simultaneous recognition of multiple speech signals, and audiovisual speech recognition. The book is appropriate for scientists and researchers in the field of speech recognition who will find an overview of the state of the art in robust speech recognition, professionals working in speech recognition who will find strategies for improving recognition results in various conditions of mismatch, and lecturers of advanced courses on speech processing or speech recognition who will find a reference and a comprehensive introduction to the field. The book assumes an understanding of the fundamentals of speech recognition using Hidden Markov Models.
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 Architectures for Deep Neural Network Based Acoustic Models for Automatic Speech Recognition written by Mayank Bhargava and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "In the recent years, Deep Neural Network-Hidden Markov Model (DNN-HMM) systems have overtaken the traditional Gaussian Mixture Model-Hidden Markov Model (GMM-HMM) systems as the state-of-the-art acoustic models in Automatic Speech Recognition (ASR). A lot of effort has been put in studying different deep learning architectures to improve ASR performance. However, most of these systems operate on the standard hand crafted spectral features which were used in the GMM-HMM systems. Recent research has shown that DNNs can operate directly on raw speech waveform input features. This thesismainly focuses on such network architectures which can operate directly on the speech waveform input features offering an alternative to standard signal processing. This thesis at first evaluates existing DNN based acoustic models trained on spectral features, analyzing various parameters affecting the performance of such networks. The ability of these DNN based systems to automatically acquire internal representation that are similar to mel-scale filter banks when fed with raw waveform input features is demonstrated. It is shown that increasing the size of the corpus helps in reducing the gap which exists between the Windowed Speech Waveform (WSW) DNNs and the Mel Frequency Spectral Coefficient (MFSC) DNNs performance. An investigation into efficient WSW DNN architectures is done and a proposed stacked bottleneck architecture is shown to reduce the gap that exists between the WSW DNN and the MFSC DNN by capturing improved spectral dynamic information. A combination of spectral features and waveformbased features is shown to improve the performance by providing additional information to the network. At last, redundancies associated with these systems are addressed and possible solutions are provided for reducing the size and complexity by using structured initialization and Singular Value Decomposition (SVD) based restructuring." --
Download or read book Hidden Conditional Random Fields for Speech Recognition written by Yun-Hsuan Sung and published by Stanford University. This book was released on 2010 with total page 161 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis investigates using a new graphical model, hidden conditional random fields (HCRFs), for speech recognition. Conditional random fields (CRFs) are discriminative sequence models that have been successfully applied to several tasks in text processing, such as named entity recognition. Recently, there has been increasing interest in applying CRFs to speech recognition due to the similarity between speech and text processing. HCRFs are CRFs augmented with hidden variables that are capable of representing the dynamic changes and variations in speech signals. HCRFs also have the ability to incorporate correlated features from both speech signals and text without making strong independence assumptions among them. This thesis presents my current research on applying HCRFs to speech recognition and HCRFs' potential to replace the current hidden Markov model (HMM) for acoustic modeling. Experimental results of phone classification, phone recognition, and speaker adaptation are presented and discussed. Our monophone HCRFs outperform both maximum mutual information estimation (MMIE) and minimum phone error (MPE) trained HMMs and achieve the-start-of-the-art performance in TIMIT phone classification and recognition tasks. We also show how to jointly train acoustic models and language models in HCRFs, which shows improvement in the results. Maximum a posterior (MAP) and maximum conditional likelihood linear regression (MCLLR) successfully adapt speaker-independent models to speaker-dependent models with a small amount of adaptation data for HCRF speaker adaptation. Finally, we explore adding gender and dialect features for phone recognition, and experimental results are presented.
Download or read book Connectionist Speech Recognition written by Hervé A. Bourlard and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: Connectionist Speech Recognition: A Hybrid Approach describes the theory and implementation of a method to incorporate neural network approaches into state of the art continuous speech recognition systems based on hidden Markov models (HMMs) to improve their performance. In this framework, neural networks (and in particular, multilayer perceptrons or MLPs) have been restricted to well-defined subtasks of the whole system, i.e. HMM emission probability estimation and feature extraction. The book describes a successful five-year international collaboration between the authors. The lessons learned form a case study that demonstrates how hybrid systems can be developed to combine neural networks with more traditional statistical approaches. The book illustrates both the advantages and limitations of neural networks in the framework of a statistical systems. Using standard databases and comparison with some conventional approaches, it is shown that MLP probability estimation can improve recognition performance. Other approaches are discussed, though there is no such unequivocal experimental result for these methods. Connectionist Speech Recognition is of use to anyone intending to use neural networks for speech recognition or within the framework provided by an existing successful statistical approach. This includes research and development groups working in the field of speech recognition, both with standard and neural network approaches, as well as other pattern recognition and/or neural network researchers. The book is also suitable as a text for advanced courses on neural networks or speech processing.
Download or read book Invariant Features and Enhanced Speaker Normalization for Automatic Speech Recognition written by Florian Müller and published by Logos Verlag Berlin GmbH. This book was released on 2013 with total page 247 pages. Available in PDF, EPUB and Kindle. Book excerpt: Automatic speech recognition systems have to handle various kinds of variabilities sufficiently well in order to achieve high recognition rates in practice. One of the variabilities that has a major impact on the performance is the vocal tract length of the speakers. Normalization of the features and adaptation of the acoustic models are commonly used methods in speech recognition systems. In contrast to that, a third approach follows the idea of extracting features with transforms that are invariant to vocal tract lengths changes. This work presents several approaches for extracting invariant features for automatic speech recognition systems. The robustness of these features under various training-test conditions is evaluated and it is described how the robustness of the features to noise can be increased. Furthermore, it is shown how the spectral effects due to different vocal tract lengths can be estimated with a registration method and how this can be used for speaker normalization.
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 Speech and Audio Processing for Coding Enhancement and Recognition written by Tokunbo Ogunfunmi and published by Springer. This book was released on 2014-10-14 with total page 347 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes the basic principles underlying the generation, coding, transmission and enhancement of speech and audio signals, including advanced statistical and machine learning techniques for speech and speaker recognition with an overview of the key innovations in these areas. Key research undertaken in speech coding, speech enhancement, speech recognition, emotion recognition and speaker diarization are also presented, along with recent advances and new paradigms in these areas.
Download or read book The Acoustic modeling Problem in Automatic Speech Recognition written by Peter F. Brown and published by . This book was released on 1987 with total page 119 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis examines the acoustic-modeling problem in automatic speech recognition from an information-theoretic point of view. This problem is to design a speech-recognition system which can extract from the speech waveform as much information as possible about the corresponding word sequence. The information extraction process is broken down into two steps: a signal processing step which converts a speech waveform into a sequence of information bearing acoustic feature vectors, and a step which models such a sequence. This thesis is primarily concerned with the use of hidden Markov models to model sequences of feature vectors which lie in a continuous space such as R sub N. It explores the trade-off between packing a lot of information into such sequences and being able to model them accurately. The difficulty of developing accurate models of continuous parameter sequences is addressed by investigating a method of parameter estimation which is specifically designed to cope with inaccurate modeling assumptions.
Download or read book Speech and Speaker Recognition written by Manfred Robert Schroeder and published by Karger Medical and Scientific Publishers. This book was released on 1985-01-01 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Acoustic Modeling for Emotion Recognition written by Koteswara Rao Anne and published by Springer. This book was released on 2015-03-14 with total page 72 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents state of art research in speech emotion recognition. Readers are first presented with basic research and applications – gradually more advance information is provided, giving readers comprehensive guidance for classify emotions through speech. Simulated databases are used and results extensively compared, with the features and the algorithms implemented using MATLAB. Various emotion recognition models like Linear Discriminant Analysis (LDA), Regularized Discriminant Analysis (RDA), Support Vector Machines (SVM) and K-Nearest neighbor (KNN) and are explored in detail using prosody and spectral features, and feature fusion techniques.
Download or read book Acoustic Modeling and Feature Selection for Speech Recognition written by Yanli Zheng and published by . This book was released on 2005 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Discriminant Training of Front end and Acoustic Modeling Stages to Heterogeneous Acoustic Environments for Multi stream Automatic Speech Recognition written by Michael Lee Shire and published by . This book was released on 2000 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Implementation of DNN HMM Acoustic Models for Phoneme Recognition written by Sihem Romdhani and published by . This book was released on 2014 with total page 85 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gaussian Mixture Model-Hidden Markov Models (GMM-HMMs) are the state-of-the-art for acoustic modeling in speech recognition. HMMs are used to model the sequential structure and the temporal variability in speech signals. However, GMMs are used to model the local spectral variability in the sound wave at each HMM state. Attempts to use Artificial Neural Networks (ANNs) to substitute GMMs in HMM-based acoustic models led to dismal results for many years. In fact, ANNs could not significantly outperform GMMs due to their shallow architectures. In addition, it was difficult to train networks with many hidden layers on large amount of data using the back-propagation learning algorithm. In recent years, with the establishment of deep learning technique, ANNs with many hidden layers have been reintroduced as an alternative to GMMs in acoustic modeling, and have shown successful results. The deep learning technique consists of a two-phase procedure. First, the ANN is generatively pre-trained using an unsupervised learning algorithm. Then, it is discriminatively fine-tuned using the back-propagation learning algorithm. The generative pre-training intends to initialize the weights of the network for better generalization performance during the discriminative phase. Combining Deep Neural Networks (DNNs) and HMMs within a single hybrid architecture for acoustic modeling have shown promising results in many speech recognition tasks. This thesis aims to empirically confirm the capability of DNNs to outperform GMMs in acoustic modeling. It also provides a systematic procedure to implement DNN-HMM acoustic models for phoneme recognition, including the implementation of a GMM-HMM baseline system. This thesis starts by providing a thorough overview of the fundamentals and background of speech recognition. The thesis then discusses DNN architecture and learning technique. In addition, the problems of GMMs and the advantages of DNNs in acoustic modeling are discussed. Finally, DNN-HMM hybrid acoustic modes for phoneme recognition are implemented. The deployed DNN is generatively pre-trained and fine-tuned to produce a posterior distribution over the states of mono-phone HMMs. The developed DNN-HMM phoneme recognition system outperform the GMM-HMM baseline on the TIMIT core test set. An in-depth investigation into the major factors behind the success of DNNs is carried out.
Download or read book Acoustic Modeling for Automatic Speech Recognition written by Remco Teunen and published by . This book was released on 2002 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: Despite the considerable progress made in recent years, automatic speech recognition is far from being a solved problem. In particular, the accuracy of a speech recognizer degrades dramatically when there is a mismatch between the training and real usage conditions. State-of-the-art speech recognizers use hidden Markov models (HMMs) and Gaussian mixture models (GMMs) with millions of parameters to model speech. The set of all these models is called the acoustic model set of the speech recognizer. The parameters are trained with speech from thousands of different speakers to capture the variabilities of speech. However, the current acoustic model set over-generalizes and is not able to capture certain constraints in speech that are relevant for recognition. For example, the acoustic model set does not take into account that the gender of a speaker cannot change within an utterance. Furthermore, experiments have shown that the acoustic model set is often not able to take advantage of the vastly increasing amount of training data that is now available with commercial applications. In this work, a novel technique for deriving discriminative Gaussian networks (GNs) from training data is presented. The Gaussian networks can be viewed as HMM/GMM models that have complex HMM structures, and simple, single Gaussian GMMs. The models are iteratively grown in complexity by splitting HMM states into two states. For each iteration the algorithm splits the states that are expected to give the most significant error rate reduction. The model parameters are discriminatively trained as well, using an improved version of the maximum mutual information (MMI) training algorithm. Evaluations using the Aurora 2 industry standard benchmark, and a small vocabulary recognition task, show that GN acoustic models are both more accurate and more robust than comparable HMM/GMM acoustic models.
