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.

In the last ten years the neuroscience of language has matured as a field. Ten years ago, neuroimaging was just being explored for neurolinguistic questions, whereas today it constitutes a routine component. At the same time there have been significant developments in linguistic and psychological theory that speak to the neuroscience of language. This book consolidates those advances into a single reference. The Handbook of the Neuroscience of Language provides a comprehensive overview of this field. Divided into five sections, section one discusses methods and techniques including clinical assessment approaches, methods of mapping the human brain, and a theoretical framework for interpreting the multiple levels of neural organization that contribute to language comprehension. Section two discusses the impact imaging techniques (PET, fMRI, ERPs, electrical stimulation of language cortex, TMS) have made to language research. Section three discusses experimental approaches to the field, including disorders at different language levels in reading as well as writing and number processing. Additionally, chapters here present computational models, discuss the role of mirror systems for language, and cover brain lateralization with respect to language. Part four focuses on language in special populations, in various disease processes, and in developmental disorders. The book ends with a listing of resources in the neuroscience of language and a glossary of items and concepts to help the novice become acquainted with the field. Editors Stemmer & Whitaker prepared this book to reflect recent developments in neurolinguistics, moving the book squarely into the cognitive neuroscience of language and capturing the developments in the field over the past 7 years. History section focuses on topics that play a current role in neurolinguistics research, aphasia syndromes, and lesion analysis Includes section on neuroimaging to reflect the dramatic changes in methodology over the past decade Experimental and clinical section reflects recent developments in the field

The care of stroke patients has changed dramatically. As well as improvements in the emergency care of the condition, there have been marked advances in our understanding, management and rehabilitation of residual deficits. This book is about the care of stroke patients, focusing on behavioural and cognitive problems. It provides a comprehensive review of the field covering the diagnostic value of these conditions, in the acute and later phases, their requirements in terms of treatment and management and the likelihood and significance of long-term disability. This book will appeal to all clinicians involved in the care of stroke patients, as well as to neuropsychologists, other rehabilitation therapists and research scientists investigating the underlying neuroscience.

Thanks to the availability of texts on the Web in recent years, increased knowledge and information have been made available to broader audiences. However, the way in which a text is written—its vocabulary, its syntax—can be difficult to read and understand for many people, especially those with poor literacy, cognitive or linguistic impairment, or those with limited knowledge of the language of the text. Texts containing uncommon words or long and complicated sentences can be difficult to read and understand by people as well as difficult to analyze by machines. Automatic text simplification is the process of transforming a text into another text which, ideally conveying the same message, will be easier to read and understand by a broader audience. The process usually involves the replacement of difficult or unknown phrases with simpler equivalents and the transformation of long and syntactically complex sentences into shorter and less complex ones. Automatic text simplification, a research topic which started 20 years ago, now has taken on a central role in natural language processing research not only because of the interesting challenges it posesses but also because of its social implications. This book presents past and current research in text simplification, exploring key issues including automatic readability assessment, lexical simplification, and syntactic simplification. It also provides a detailed account of machine learning techniques currently used in simplification, describes full systems designed for specific languages and target audiences, and offers available resources for research and development together with text simplification evaluation techniques.