Download or read book Language Modeling for Automatic Speech Recognition of Inflective Languages written by Gregor Donaj and published by Springer. This book was released on 2016-08-29 with total page 77 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers language modeling and automatic speech recognition for inflective languages (e.g. Slavic languages), which represent roughly half of the languages spoken in Europe. These languages do not perform as well as English in speech recognition systems and it is therefore harder to develop an application with sufficient quality for the end user. The authors describe the most important language features for the development of a speech recognition system. This is then presented through the analysis of errors in the system and the development of language models and their inclusion in speech recognition systems, which specifically address the errors that are relevant for targeted applications. The error analysis is done with regard to morphological characteristics of the word in the recognized sentences. The book is oriented towards speech recognition with large vocabularies and continuous and even spontaneous speech. Today such applications work with a rather small number of languages compared to the number of spoken languages.
Download or read book Automatic Speech Recognition and Translation for Low Resource Languages written by L. Ashok Kumar and published by John Wiley & Sons. This book was released on 2024-03-28 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt: AUTOMATIC SPEECH RECOGNITION and TRANSLATION for LOW-RESOURCE LANGUAGES This book is a comprehensive exploration into the cutting-edge research, methodologies, and advancements in addressing the unique challenges associated with ASR and translation for low-resource languages. Automatic Speech Recognition and Translation for Low Resource Languages contains groundbreaking research from experts and researchers sharing innovative solutions that address language challenges in low-resource environments. The book begins by delving into the fundamental concepts of ASR and translation, providing readers with a solid foundation for understanding the subsequent chapters. It then explores the intricacies of low-resource languages, analyzing the factors that contribute to their challenges and the significance of developing tailored solutions to overcome them. The chapters encompass a wide range of topics, ranging from both the theoretical and practical aspects of ASR and translation for low-resource languages. The book discusses data augmentation techniques, transfer learning, and multilingual training approaches that leverage the power of existing linguistic resources to improve accuracy and performance. Additionally, it investigates the possibilities offered by unsupervised and semi-supervised learning, as well as the benefits of active learning and crowdsourcing in enriching the training data. Throughout the book, emphasis is placed on the importance of considering the cultural and linguistic context of low-resource languages, recognizing the unique nuances and intricacies that influence accurate ASR and translation. Furthermore, the book explores the potential impact of these technologies in various domains, such as healthcare, education, and commerce, empowering individuals and communities by breaking down language barriers. Audience The book targets researchers and professionals in the fields of natural language processing, computational linguistics, and speech technology. It will also be of interest to engineers, linguists, and individuals in industries and organizations working on cross-lingual communication, accessibility, and global connectivity.
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 Language Modeling for Automatic Speech Recognition in Telehealth written by Xiaojia Zhang and published by . This book was released on 2005 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Standard statistic n-gram language models play a critical and indispensable role in automatic speech recognition (ASR) applications. Though helpful to ASR, it suffers from a practical problem when lacking sufficient in-domain training data that come from same or similar sources as the task text. In order to improve language model performance, various datasets need to be used to supplement the in-domain training data. This thesis investigates effective approaches to language modeling for telehealth which consists of doctor-patient conversation speech in medical specialty domain. Efforts were made to collect and analyze various datasets for training as well as to find a method for modeling target language. By effectively defining word classes, and by combining class and word trigram language models trained separately from in-domain and out-of-domain datasets, large improvements were achieved in perplexity reduction over a baseline word trigram language model that simply interpolates word trigram models trained from different data sources.
Download or read book Language Modelling for Automatic Speech Recognition written by L. Dodd and published by . This book was released on 1987 with total page 22 pages. Available in PDF, EPUB and Kindle. Book excerpt: This memorandum reviews recent studies and developments in methods of language modelling which are specifically relevant to automatic speech recognition (ASR). An introduction is given to the general area of language models and the ways of formalising linguistic knowledge. Various techniques for applying phonological, syntactic and semantic constraints to ASR are discussed. The review covers papers written as early as the 1970's but the emphasis is on the more recent developments and techniques which are now being used in speech research. The formal methods of applying linguistic constraints are discussed and criticised according to their suitability for the speech research work carried out at RSRE.
Download or read book Speech Language Processing written by Dan Jurafsky and published by Pearson Education India. This book was released on 2000-09 with total page 912 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Topic Based Language Modeling for Automatic Speech Recognition written by Raghunandan Sampath Kumaran and published by . This book was released on 2005 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Mathematical Foundations of Speech and Language Processing written by Mark Johnson and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: Speech and language technologies continue to grow in importance as they are used to create natural and efficient interfaces between people and machines, and to automatically transcribe, extract, analyze, and route information from high-volume streams of spoken and written information. The workshops on Mathematical Foundations of Speech Processing and Natural Language Modeling were held in the Fall of 2000 at the University of Minnesota's NSF-sponsored Institute for Mathematics and Its Applications, as part of a "Mathematics in Multimedia" year-long program. Each workshop brought together researchers in the respective technologies on the one hand, and mathematicians and statisticians on the other hand, for an intensive week of cross-fertilization. There is a long history of benefit from introducing mathematical techniques and ideas to speech and language technologies. Examples include the source-channel paradigm, hidden Markov models, decision trees, exponential models and formal languages theory. It is likely that new mathematical techniques, or novel applications of existing techniques, will once again prove pivotal for moving the field forward. This volume consists of original contributions presented by participants during the two workshops. Topics include language modeling, prosody, acoustic-phonetic modeling, and statistical methodology.
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 Statistical Language Modelling for Automatic Speech Recognition of Russian and English written by Edward William Daniel Whittaker and published by . This book was released on 2000 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Adaptation of Statistical Language Models for Automatic Speech Recognition written by P. R. Clarkson and published by . This book was released on 1999 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Recurrent Neural Network Language Models for Automatic Speech Recognition written by Siva Reddy Gangireddy and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book An Integrated Language Model for Automatic Speech Recognition written by Harvey Lloyd-Thomas and published by . This book was released on 1995 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book An Exploration of Composite Language Modeling for Speech Recognition written by Xiaolin Xie and published by . This book was released on 2013 with total page 67 pages. Available in PDF, EPUB and Kindle. Book excerpt: Language models are one of the most critical knowledge sources of automatic speech recognition (ASR) systems. In the past decades, many language models have been developed, and some have proved useful and successful in speech recognition systems. However, almost all language models only capture one or two aspects of natural language. This study aims to investigate the effects of a syntactic, semantic, and lexical language model on speech recognition. In this study, we refer this language model as the composite language model (CLM). The parameters of the CLM in our study are distributed among hundreds of computer nodes in a supercomputer because they are too large to be stored in just one computer node. A distributed application has been developed to implement two speech rescoring techniques by using the CLM: lattice rescoring and confusion network rescoring. Experiments on a Wall Street Journal task have shown that using CLM to rescore word lattices and confusion networks have led to improvements in word accuracy over the commonly used trigram language model, with the latter offering a larger performance gain.
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 Deep Learning for NLP and Speech Recognition written by Uday Kamath and published by Springer. This book was released on 2019-06-10 with total page 621 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook explains Deep Learning Architecture, with applications to various NLP Tasks, including Document Classification, Machine Translation, Language Modeling, and Speech Recognition. With the widespread adoption of deep learning, natural language processing (NLP),and speech applications in many areas (including Finance, Healthcare, and Government) there is a growing need for one comprehensive resource that maps deep learning techniques to NLP and speech and provides insights into using the tools and libraries for real-world applications. Deep Learning for NLP and Speech Recognition explains recent deep learning methods applicable to NLP and speech, provides state-of-the-art approaches, and offers real-world case studies with code to provide hands-on experience. Many books focus on deep learning theory or deep learning for NLP-specific tasks while others are cookbooks for tools and libraries, but the constant flux of new algorithms, tools, frameworks, and libraries in a rapidly evolving landscape means that there are few available texts that offer the material in this book. The book is organized into three parts, aligning to different groups of readers and their expertise. The three parts are: Machine Learning, NLP, and Speech Introduction The first part has three chapters that introduce readers to the fields of NLP, speech recognition, deep learning and machine learning with basic theory and hands-on case studies using Python-based tools and libraries. Deep Learning Basics The five chapters in the second part introduce deep learning and various topics that are crucial for speech and text processing, including word embeddings, convolutional neural networks, recurrent neural networks and speech recognition basics. Theory, practical tips, state-of-the-art methods, experimentations and analysis in using the methods discussed in theory on real-world tasks. Advanced Deep Learning Techniques for Text and Speech The third part has five chapters that discuss the latest and cutting-edge research in the areas of deep learning that intersect with NLP and speech. Topics including attention mechanisms, memory augmented networks, transfer learning, multi-task learning, domain adaptation, reinforcement learning, and end-to-end deep learning for speech recognition are covered using case studies.
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
