Download or read book A Study on Acoustic Modeling and Adaptation in Hmm Based Speech Recognition written by Bin Ma and published by . This book was released on 2017-01-27 with total page 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 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 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 Phoneme Class Based Adaptation for Mismatch Acoustic Modeling of Distant Noisy Speech written by Seckin Uluskan and published by . This book was released on 2012 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: Distant speech capture in lecture halls and auditoriums offer unique challenges in algorithm development for automatic speech recognition. A new adaptation strategy for distant noisy speech is created by phoneme class based approaches for context-independent acoustic models. Unlike previous approaches such as maximum likelihood linear regression and maximum a posteriori (MLLR and MAP) adaptation which adapts the acoustic model to the features, the proposed phoneme-class based adaptation (PCBA) strategy adapts the distant data features to the present acoustic model which was previously trained on close microphone speech. The essence of PCBA is to create a transformation strategy which makes the distribution of phoneme-classes of distant noisy speech be similar to those of a close talk microphone acoustic model in a multidimensional mel-frequency cepstral coefficient (MFCC) space. PCBA creates a mean, orientation and variance adaptation scheme for each phoneme class to compensate for the overall mismatch of distance (talker to far-field microphone). New adapted features, and new and improved acoustic models produced by PCBA are shown to outperform those created by MLLR-MAP adaptation for automatic speech recognition (ASR) and keyword spotting (KWS). PCBA offers a new powerful understanding in acoustic-modeling of distant speech.

Download or read book Speech Recognition and Coding written by Antonio J. Rubio Ayuso and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 517 pages. Available in PDF, EPUB and Kindle. Book excerpt: Based on a NATO Advanced Study Institute held in 1993, this book addresses recent advances in automatic speech recognition and speech coding. The book contains contributions by many of the most outstanding researchers from the best laboratories worldwide in the field. The contributions have been grouped into five parts: on acoustic modeling; language modeling; speech processing, analysis and synthesis; speech coding; and vector quantization and neural nets. For each of these topics, some of the best-known researchers were invited to give a lecture. In addition to these lectures, the topics were complemented with discussions and presentations of the work of those attending. Altogether, the reader is given a wide perspective on recent advances in the field and will be able to see the trends for future work.

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 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 Nonlinear Speech Analysis and Acoustic Model Adaptation with Applications to Stress Classification and Speech Recognition written by Guojun Zhou and published by . This book was released on 1999 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Robust Speech Recognition in Embedded Systems and PC Applications written by Jean-Claude Junqua and published by Springer Science & Business Media. This book was released on 2006-04-18 with total page 193 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robust Speech Recognition in Embedded Systems and PC Applications provides a link between the technology and the application worlds. As speech recognition technology is now good enough for a number of applications and the core technology is well established around hidden Markov models many of the differences between systems found in the field are related to implementation variants. We distinguish between embedded systems and PC-based applications. Embedded applications are usually cost sensitive and require very simple and optimized methods to be viable. Robust Speech Recognition in Embedded Systems and PC Applications reviews the problems of robust speech recognition, summarizes the current state of the art of robust speech recognition while providing some perspectives, and goes over the complementary technologies that are necessary to build an application, such as dialog and user interface technologies. Robust Speech Recognition in Embedded Systems and PC Applications is divided into five chapters. The first one reviews the main difficulties encountered in automatic speech recognition when the type of communication is unknown. The second chapter focuses on environment-independent/adaptive speech recognition approaches and on the mainstream methods applicable to noise robust speech recognition. The third chapter discusses several critical technologies that contribute to making an application usable. It also provides some design recommendations on how to design prompts, generate user feedback and develop speech user interfaces. The fourth chapter reviews several techniques that are particularly useful for embedded systems or to decrease computational complexity. It also presents some case studies for embedded applications and PC-based systems. Finally, the fifth chapter provides a future outlook for robust speech recognition, emphasizing the areas that the author sees as the most promising for the future. Robust Speech Recognition in Embedded Systems and PC Applications serves as a valuable reference and although not intended as a formal University textbook, contains some material that can be used for a course at the graduate or undergraduate level. It is a good complement for the book entitled Robustness in Automatic Speech Recognition: Fundamentals and Applications co-authored by the same author.

Download or read book Techniques for Noise Robustness in Automatic Speech Recognition written by Tuomas Virtanen and published by John Wiley & Sons. This book was released on 2012-09-19 with total page 514 pages. Available in PDF, EPUB and Kindle. Book excerpt: Automatic speech recognition (ASR) systems are finding increasing use in everyday life. Many of the commonplace environments where the systems are used are noisy, for example users calling up a voice search system from a busy cafeteria or a street. This can result in degraded speech recordings and adversely affect the performance of speech recognition systems. As the use of ASR systems increases, knowledge of the state-of-the-art in techniques to deal with such problems becomes critical to system and application engineers and researchers who work with or on ASR technologies. This book presents a comprehensive survey of the state-of-the-art in techniques used to improve the robustness of speech recognition systems to these degrading external influences. Key features: Reviews all the main noise robust ASR approaches, including signal separation, voice activity detection, robust feature extraction, model compensation and adaptation, missing data techniques and recognition of reverberant speech. Acts as a timely exposition of the topic in light of more widespread use in the future of ASR technology in challenging environments. Addresses robustness issues and signal degradation which are both key requirements for practitioners of ASR. Includes contributions from top ASR researchers from leading research units in the field

Download or read book Acoustic Model Adaptation for Recognition of Dysarthric Speech written by and published by . This book was released on 2012 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 Acoustic Model and Pronunciation Adaptation in Automatic Speech Recognition written by Yongxin Zhang and published by . This book was released on 2006 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 Speaker Adaptation of Deep Neural Network Acoustic Models Using Gaussian Mixture Model Framework in Automatic Speech Recognition Systems written by Natalia Tomashenko and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Differences between training and testing conditions may significantly degrade recognition accuracy in automatic speech recognition (ASR) systems. Adaptation is an efficient way to reduce the mismatch between models and data from a particular speaker or channel. There are two dominant types of acoustic models (AMs) used in ASR: Gaussian mixture models (GMMs) and deep neural networks (DNNs). The GMM hidden Markov model (GMM-HMM) approach has been one of the most common technique in ASR systems for many decades. Speaker adaptation is very effective for these AMs and various adaptation techniques have been developed for them. On the other hand, DNN-HMM AMs have recently achieved big advances and outperformed GMM-HMM models for various ASR tasks. However, speaker adaptation is still very challenging for these AMs. Many adaptation algorithms that work well for GMMs systems cannot be easily applied to DNNs because of the different nature of these models. The main purpose of this thesis is to develop a method for efficient transfer of adaptation algorithms from the GMM framework to DNN models. A novel approach for speaker adaptation of DNN AMs is proposed and investigated. The idea of this approach is based on using so-called GMM-derived features as input to a DNN. The proposed technique provides a general framework for transferring adaptation algorithms, developed for GMMs, to DNN adaptation. It is explored for various state-of-the-art ASR systems and is shown to be effective in comparison with other speaker adaptation techniques and complementary to them.

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.