Download or read book Multi level Acoustic Modeling for Automatic Speech Recognition written by Hung-An Chang (Ph. D.) and published by . This book was released on 2012 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: Context-dependent acoustic modeling is commonly used in large-vocabulary Automatic Speech Recognition (ASR) systems as a way to model coarticulatory variations that occur during speech production. Typically, the local phoneme context is used as a means to define context-dependent units. Because the number of possible context-dependent units can grow exponentially with the length of the contexts, many units will not have enough training examples to train a robust model, resulting in a data sparsity problem. For nearly two decades, this data sparsity problem has been dealt with by a clustering-based framework which systematically groups different context-dependent units into clusters such that each cluster can have enough data. Although dealing with the data sparsity issue, the clustering-based approach also makes all context-dependent units within a cluster have the same acoustic score, resulting in a quantization effect that can potentially limit the performance of the context-dependent model. In this work, a multi-level acoustic modeling framework is proposed to address both the data sparsity problem and the quantization effect. Under the multi-level framework, each context-dependent unit is associated with classifiers that target multiple levels of contextual resolution, and the outputs of the classifiers are linearly combined for scoring during recognition. By choosing the classifiers judiciously, both the data sparsity problem and the quantization effect can be dealt with. The proposed multi-level framework can also be integrated into existing large-vocabulary ASR systems, such as FST-based ASR systems, and is compatible with state-of-the-art error reduction techniques for ASR systems, such as discriminative training methods. Multiple sets of experiments have been conducted to compare the performance of the clustering-based acoustic model and the proposed multi-level model. In a phonetic recognition experiment on TIMIT, the multi-level model has about 8% relative improvement in terms of phone error rate, showing that the multi-level framework can help improve phonetic prediction accuracy. In a large-vocabulary transcription task, combining the proposed multi-level modeling framework with discriminative training can provide more than 20% relative improvement over a clustering baseline model in terms of Word Error Rate (WER), showing that the multi-level framework can be integrated into existing large-vocabulary decoding frameworks and that it combines well with discriminative training methods. In speaker adaptive transcription task, the multi-level model has about 14% relative WER improvement, showing that the proposed framework can adapt better to new speakers, and potentially to new environments than the conventional clustering-based approach.

Download or read book Multilayer Perceptron Based Hierarchical Acoustic Modeling for Automatic Speech Recognition written by Joel Praveen Pinto and published by . This book was released on 2010 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 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 Automatic Speech and Speaker Recognition written by Chin-Hui Lee and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 524 pages. Available in PDF, EPUB and Kindle. Book excerpt: Research in the field of automatic speech and speaker recognition has made a number of significant advances in the last two decades, influenced by advances in signal processing, algorithms, architectures, and hardware. These advances include: the adoption of a statistical pattern recognition paradigm; the use of the hidden Markov modeling framework to characterize both the spectral and the temporal variations in the speech signal; the use of a large set of speech utterance examples from a large population of speakers to train the hidden Markov models of some fundamental speech units; the organization of speech and language knowledge sources into a structural finite state network; and the use of dynamic, programming based heuristic search methods to find the best word sequence in the lexical network corresponding to the spoken utterance. Automatic Speech and Speaker Recognition: Advanced Topics groups together in a single volume a number of important topics on speech and speaker recognition, topics which are of fundamental importance, but not yet covered in detail in existing textbooks. Although no explicit partition is given, the book is divided into five parts: Chapters 1-2 are devoted to technology overviews; Chapters 3-12 discuss acoustic modeling of fundamental speech units and lexical modeling of words and pronunciations; Chapters 13-15 address the issues related to flexibility and robustness; Chapter 16-18 concern the theoretical and practical issues of search; Chapters 19-20 give two examples of algorithm and implementational aspects for recognition system realization. Audience: A reference book for speech researchers and graduate students interested in pursuing potential research on the topic. May also be used as a text for advanced courses on the subject.

Download or read book Distinct Acoustic Modeling for Automatic Speech Recognition written by Yu Ting Ko and published by . This book was released on 2014 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 Dynamic Speech Models written by Li Deng and published by Springer Nature. This book was released on 2022-05-31 with total page 105 pages. Available in PDF, EPUB and Kindle. Book excerpt: Speech dynamics refer to the temporal characteristics in all stages of the human speech communication process. This speech “chain” starts with the formation of a linguistic message in a speaker's brain and ends with the arrival of the message in a listener's brain. Given the intricacy of the dynamic speech process and its fundamental importance in human communication, this monograph is intended to provide a comprehensive material on mathematical models of speech dynamics and to address the following issues: How do we make sense of the complex speech process in terms of its functional role of speech communication? How do we quantify the special role of speech timing? How do the dynamics relate to the variability of speech that has often been said to seriously hamper automatic speech recognition? How do we put the dynamic process of speech into a quantitative form to enable detailed analyses? And finally, how can we incorporate the knowledge of speech dynamics into computerized speech analysis and recognition algorithms? The answers to all these questions require building and applying computational models for the dynamic speech process. What are the compelling reasons for carrying out dynamic speech modeling? We provide the answer in two related aspects. First, scientific inquiry into the human speech code has been relentlessly pursued for several decades. As an essential carrier of human intelligence and knowledge, speech is the most natural form of human communication. Embedded in the speech code are linguistic (as well as para-linguistic) messages, which are conveyed through four levels of the speech chain. Underlying the robust encoding and transmission of the linguistic messages are the speech dynamics at all the four levels. Mathematical modeling of speech dynamics provides an effective tool in the scientific methods of studying the speech chain. Such scientific studies help understand why humans speak as they do and how humans exploit redundancy and variability by way of multitiered dynamic processes to enhance the efficiency and effectiveness of human speech communication. Second, advancement of human language technology, especially that in automatic recognition of natural-style human speech is also expected to benefit from comprehensive computational modeling of speech dynamics. The limitations of current speech recognition technology are serious and are well known. A commonly acknowledged and frequently discussed weakness of the statistical model underlying current speech recognition technology is the lack of adequate dynamic modeling schemes to provide correlation structure across the temporal speech observation sequence. Unfortunately, due to a variety of reasons, the majority of current research activities in this area favor only incremental modifications and improvements to the existing HMM-based state-of-the-art. For example, while the dynamic and correlation modeling is known to be an important topic, most of the systems nevertheless employ only an ultra-weak form of speech dynamics; e.g., differential or delta parameters. Strong-form dynamic speech modeling, which is the focus of this monograph, may serve as an ultimate solution to this problem. After the introduction chapter, the main body of this monograph consists of four chapters. They cover various aspects of theory, algorithms, and applications of dynamic speech models, and provide a comprehensive survey of the research work in this area spanning over past 20~years. This monograph is intended as advanced materials of speech and signal processing for graudate-level teaching, for professionals and engineering practioners, as well as for seasoned researchers and engineers specialized in speech processing

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 Robust Acoustic Modeling and Front end Design for Distant Speech Recognition written by Seyedmahdad Mirsamadi and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, there has been a significant increase in the popularity of voice-enabled technologies which use human speech as the primary interface with machines. Recent advancements in acoustic modeling and feature design have increased the accuracy of Automatic Speech Recognition (ASR) to levels that enable voice interfaces to be used in many applications. However, much of the current performance is dependent on the use of close-talking microphones, (i.e., scenarios in which the user speaks directly into a hand-held or body-worn microphone). There is still a rather large performance gap experienced in distant-talking scenarios in which speech is recorded by far-field microphones that are placed at a distance from the speaker. In such scenarios, the distorting effects of distance (such as room reverberation and environment noise) make the recognition task significantly more challenging. In this dissertation, we propose novel approaches for designing a distant-talking ASR front-end as well as training robust acoustic models to reduce the existing gap between far-field and close-talking ASR performance. Specifically, we i) propose a novel multi-channel front-end enhancement algorithm for improved ASR in reverberant rooms using distributed non-uniform microphone arrays with random unknown locations; ii) propose a novel neural network model training approach using adversarial training to improve the robustness of multi-condition acoustic models that are trained directly on far-field data; iii) study alternate neural network adaptation strategies for far-field adaptation to the acoustic properties of specific target environments. Experimental results are provided based on far-field benchmark tasks and datasets which demonstrate the effectiveness of the proposed approaches for increasing far-field robustness in ASR. Based on experiments using reverberated TIMIT sentences, the proposed multi-channel front-end provides WER improvements of +21.5% and +37.7% in two-channel and four-channel scenarios over a single-channel scenario in which the channel with best signal quality is selected. On the acoustic modeling side and based on results of experiments on AMI corpus, the proposed multi-domain training approach provides a relative character error rate reduction of +3.3% with respect to a conventional multi-condition trained baseline, and +25.4% with respect to a clean-trained baseline.

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 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 Knowledge Transfer by Sharing Acoustic model Parameters for Automatic Speech Recognition written by Aanchan Mohan and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "The objective of this thesis is to develop efficient methods for the transfer of knowledge between languages and speakers by sharing acoustic model parameters for automatic speech recognition (ASR). Knowledge transfer between languages is often useful when only a limited amount of transcribed data is available for ASR system development in a target language. Additionally, boot-strapping acoustic phonetic knowledge is also seen to improve ASR performance when adequate training data is available. These scenarios are used as examples to study issues in acoustic-phonetic knowledge-transfer for ASR. Furthermore, the parameters that characterize speaker variability could often be thought to lie in a low-dimensional subspace or a manifold. Parameters for a new test speaker are often estimated with knowledge transfer from training speaker information that is parametrized as a set of subspace vectors or low-dimensional embeddings on a manifold. The technical contributions in this thesis are as follows. First, acoustic mismatch due to different recording instruments and background conditions poses a problem when training a single multi-lingual statistical model on data from multiple languages. The subspace Gaussian mixture model (SGMM), which allows for natural sharing of model parameters between acoustic-phonetic units of different languages is used in this study. A two-stage procedure is proposed to compensate for speaker variability and environmental variability, prior to multi-lingual acoustic model training. As a result of this compensation procedure, ASR performance improvements are observed for all languages used in multi-lingual acoustic model training. Experimental results are presented on Hindi and Marathi speech data on a small-vocabulary agricultural commodities task. With only one hour of available Hindi data, multi-lingual acoustic model training with Marathi is seen to improve Hindi language ASR performance significantly compared to mono-lingual training. Second, to reduce the number of context-dependent errors in Hindi, an algorithm for borrowing state-level SGMM parameters from Marathi in the multi-lingual SGMM acoustic model is proposed. A statistically significant improvement is observed in Hindi language ASR. Furthermore, in order to reduce the number of parameters in the Hindi-Marathi multi-lingual acoustic model, the use of semi-tied covariance (STC) instead of full-covariance matrices is proposed. With a reduction of a factor of five relative to full-covariance parameters, similar ASR accuracy is maintained through the use of STCs. Third, the use of multi-task training for multi-lingual neural network acoustic models is studied. The use of multi-task training provides state of the art results on a well-known large vocabulary read speech task. Experiments on cross-language adaptation when only a limited amount of target language data is available are also presented. To reduce space and time-complexity to train these networks the impact of low-rank matrix factorization of the weight matrix in the final layer is presented. Finally, parameters that model speaker variability in Linear Input Network (LIN) based speaker adaptation for deep neural networks are assumed to lie on a manifold. Obtaining speaker specific parameters is treated as a task in a multi-task learning problem. Task parameters and their low-dimensional projections are assumed to lie on a manifold. A manifold constraint as a regularization term is introduced into the cost function for estimating LIN speaker parameters during test time. Experimental results are presented to evaluate this approach." --

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 Ensemble Acoustic Modeling in Automatic Speech Recognition written by Xin Chen and published by . This book was released on 2011 with total page 106 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this dissertation, several new approaches of using data sampling to construct an Ensemble of Acoustic Models (EAM) for speech recognition are proposed. A straightforward method of data sampling is Cross Validation (CV) data partition. In the direction of improving inter-model diversity within an EAM for speaker independent speech recognition, we propose Speaker Clustering (SC) based data sampling. In the direction of improving base model quality as well as inter-model diversity, we further investigate the effects of several successful techniques of single model training in speech recognition on the proposed ensemble acoustic models, including Cross Validation Expectation Maximization (CVEM), Discriminative Training (DT), and Multiple Layer Perceptron (MLP) features. We have evaluated the proposed methods on TIMIT phoneme recognition task as well as on a telemedicine automatic captioning task. The proposed EAMs have led to significant improvements in recognition accuracy over conventional Hidden Markov Model (HMM) baseline systems, and the integration of EAM with CVEM, DT and MLP has also significantly improved the accuracy performances of CVEM, DT, and MLP based single model systems. We further investigated the largely unstudied factor of inter-model diversity, and proposed several methods to explicit measure inter-model diversity. We demonstrate a positive relation between enlarging inter-model diversity and increasing EAM quality. Compacting the acoustic model to a reasonable size for practical applications while maintaining a reasonable performance is needed for EAM. Toward this goal, in this dissertation, we discuss and investigate several distance measures and proposed global optimization algorithms for clustering methods. We also proposed an explicit PDT (EPDT) state tying approach that allows Phoneme data Sharing (PS) for its potential capability in accommodating pronunciation variations.

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 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.