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 Statistical Language Modeling for Automatic Speech Recognition of Agglutinative Languages written by Ebru Arısoy and published by . This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Language Modeling for Automatic Speech Recognition of Agglutinative Languages.

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 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 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 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 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 Spoken Language Understanding written by Gokhan Tur and published by John Wiley & Sons. This book was released on 2011-05-03 with total page 443 pages. Available in PDF, EPUB and Kindle. Book excerpt: Spoken language understanding (SLU) is an emerging field in between speech and language processing, investigating human/ machine and human/ human communication by leveraging technologies from signal processing, pattern recognition, machine learning and artificial intelligence. SLU systems are designed to extract the meaning from speech utterances and its applications are vast, from voice search in mobile devices to meeting summarization, attracting interest from both commercial and academic sectors. Both human/machine and human/human communications can benefit from the application of SLU, using differing tasks and approaches to better understand and utilize such communications. This book covers the state-of-the-art approaches for the most popular SLU tasks with chapters written by well-known researchers in the respective fields. Key features include: Presents a fully integrated view of the two distinct disciplines of speech processing and language processing for SLU tasks. Defines what is possible today for SLU as an enabling technology for enterprise (e.g., customer care centers or company meetings), and consumer (e.g., entertainment, mobile, car, robot, or smart environments) applications and outlines the key research areas. Provides a unique source of distilled information on methods for computer modeling of semantic information in human/machine and human/human conversations. This book can be successfully used for graduate courses in electronics engineering, computer science or computational linguistics. Moreover, technologists interested in processing spoken communications will find it a useful source of collated information of the topic drawn from the two distinct disciplines of speech processing and language processing under the new area of SLU.

Download or read book Spoken Language Processing written by Xuedong Huang and published by Prentice Hall. This book was released on 2001 with total page 1018 pages. Available in PDF, EPUB and Kindle. Book excerpt: Remarkable progress is being made in spoken language processing, but many powerful techniques have remained hidden in conference proceedings and academic papers, inaccessible to most practitioners. In this book, the leaders of the Speech Technology Group at Microsoft Research share these advances -- presenting not just the latest theory, but practical techniques for building commercially viable products.KEY TOPICS: Spoken Language Processing draws upon the latest advances and techniques from multiple fields: acoustics, phonology, phonetics, linguistics, semantics, pragmatics, computer science, electrical engineering, mathematics, syntax, psychology, and beyond. The book begins by presenting essential background on speech production and perception, probability and information theory, and pattern recognition. The authors demonstrate how to extract useful information from the speech signal; then present a variety of contemporary speech recognition techniques, including hidden Markov models, acoustic and language modeling, and techniques for improving resistance to environmental noise. Coverage includes decoders, search algorithms, large vocabulary speech recognition techniques, text-to-speech, spoken language dialog management, user interfaces, and interaction with non-speech interface modalities. The authors also present detailed case studies based on Microsoft's advanced prototypes, including the Whisper speech recognizer, Whistler text-to-speech system, and MiPad handheld computer.MARKET: For anyone involved with planning, designing, building, or purchasing spoken language technology.

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 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 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 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 Language Models for Automatic Speech Recognition written by A. Corazza and published by . This book was released on 1993 with total page 29 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Automatic Acquisition of Language Models for Speech Recognition written by Michael Kyle McCandless and published by . This book was released on 1994 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "This thesis focuses on the automatic acquisition of language structure and the subsequent use of the learned language structure to improve the performance of a speech recognition system. First, we develop a grammar inference process which is able to learn a grammar describing a large set of training sentences. The process of acquiring this grammar is one of generalization so that the resulting grammar predicts likely sentences beyond those contained in the training set. From the grammar we construct a novel probabilistic language model called the phrase class n-gram model (PCNG), which is a natural generalization of the world class n-gram model [11] to phrase classes. This model utilizes the grammar in such a way that it maintains full coverage of any test set while at the same time reducing the complexity, or number of parameters, of the resulting predictive model. Positive results are shown in terms of perplexity of the acquired phrase class n-gram models and in terms of reduction of word error rates for a speech recognition system."

Download or read book Recent Trends in Computational Intelligence written by Ali Sadollah and published by BoD – Books on Demand. This book was released on 2020-05-06 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: Traditional models struggle to cope with complexity, noise, and the existence of a changing environment, while Computational Intelligence (CI) offers solutions to complicated problems as well as reverse problems. The main feature of CI is adaptability, spanning the fields of machine learning and computational neuroscience. CI also comprises biologically-inspired technologies such as the intellect of swarm as part of evolutionary computation and encompassing wider areas such as image processing, data collection, and natural language processing. This book aims to discuss the usage of CI for optimal solving of various applications proving its wide reach and relevance. Bounding of optimization methods and data mining strategies make a strong and reliable prediction tool for handling real-life applications.