Stochastically-Based Semantic Analysis investigates the problem of automatic natural language understanding in a spoken language dialog system. The focus is on the design of a stochastic parser and its evaluation with respect to a conventional rule-based method. Stochastically-Based Semantic Analysis will be of most interest to researchers in artificial intelligence, especially those in natural language processing, computational linguistics, and speech recognition. It will also appeal to practicing engineers who work in the area of interactive speech systems.
Waibel, (computer science, Carnegie-Mellon U.), focuses on the prosodic cues (e.g., pitch, intensity, rhythm, temporal relationships, stress) that are critical to human speech perception. No index. Annotation copyrighted by Book News, Inc., Portland, OR
Tato publikace je sborníkem 21 příspěvků, přednesených na 9. ročníku konference „Teaching and Learning Corpora“, která se uskutečnila na Masarykově univerzitě v létě 2010. Statě byly vybrány na základě dvou anonymních posudků, poskytnutých vědeckou radou konané konference. Kniha se zabývá rozmanitými způsoby využití jazykových korpusů při výuce a při studiu cizího jazyka, a je rozdělena do čtyř oddílů. Oddíly 1 a 2 pohlížejí na korpus jako vstupní zdroj, zkoumají nejdříve obecně jak mohou korpusy obohatit výuku jazyka, poté na konkrétních případech ukazují, jak převést poznatky do praxe, a nakonec hodnotí jednotlivé využití korpusů studenty. Oddíly 3 a 4 tematizují korpus jako výstup, což představuje především srovnání s korpusy rodilých mluvčích a následnou identifikaci „chyb“ či problémových oblastí, ale také ukazují, co studenti mohou vědět a skutečně ví v různých úrovních pokročilosti, a pokouší se zodpovědět na otázku, co nám tyto informace říkají o samotném procesu učení.
Follow the blueprint in this book to launch a library DIY community history digitization program—one that provides the access and fosters engagement with patrons to sustain the program over time. Internet technologies have enabled anyone to tell their story—and to find out their own unknown story. Libraries are seeing increased interest in community and family history and in genealogy, as well as heightened demand for access to personal and community history materials in digital format. The opportunity exists for libraries to benefit their communities by providing these in-demand, digitized historical materials optimized for researchers at the individual level. Digitizing Your Community's History: The Innovative Librarian's Guide provides you with step-by-step directions for launching a DIY digitization program for personal and community historical materials. It covers the process of setting up a digitization program, training customers to use the equipment, best practices for storing digitized material, and tips for engaging the community in local history, such as ideas for exhibiting materials and programs for genealogy and family history. Just as importantly, the author addresses how to explain the benefits of programs like these to library stakeholders and supplies recommendations on sustaining library community history programs through access and engagement. The book also provides supplemental materials that include templates and programming ideas, lists of recommended software and apps, and recommended specifications for equipment and for file storage.
Supervised sequence labelling is a vital area of machine learning, encompassing tasks such as speech, handwriting and gesture recognition, protein secondary structure prediction and part-of-speech tagging. Recurrent neural networks are powerful sequence learning tools—robust to input noise and distortion, able to exploit long-range contextual information—that would seem ideally suited to such problems. However their role in large-scale sequence labelling systems has so far been auxiliary. The goal of this book is a complete framework for classifying and transcribing sequential data with recurrent neural networks only. Three main innovations are introduced in order to realise this goal. Firstly, the connectionist temporal classification output layer allows the framework to be trained with unsegmented target sequences, such as phoneme-level speech transcriptions; this is in contrast to previous connectionist approaches, which were dependent on error-prone prior segmentation. Secondly, multidimensional recurrent neural networks extend the framework in a natural way to data with more than one spatio-temporal dimension, such as images and videos. Thirdly, the use of hierarchical subsampling makes it feasible to apply the framework to very large or high resolution sequences, such as raw audio or video. Experimental validation is provided by state-of-the-art results in speech and handwriting recognition.
The need for automatic speech recognition systems to be robust with respect to changes in their acoustical environment has become more widely appreciated in recent years, as more systems are finding their way into practical applications. Although the issue of environmental robustness has received only a small fraction of the attention devoted to speaker independence, even speech recognition systems that are designed to be speaker independent frequently perform very poorly when they are tested using a different type of microphone or acoustical environment from the one with which they were trained. The use of microphones other than a "close talking" headset also tends to severely degrade speech recognition -performance. Even in relatively quiet office environments, speech is degraded by additive noise from fans, slamming doors, and other conversations, as well as by the effects of unknown linear filtering arising reverberation from surface reflections in a room, or spectral shaping by microphones or the vocal tracts of individual speakers. Speech-recognition systems designed for long-distance telephone lines, or applications deployed in more adverse acoustical environments such as motor vehicles, factory floors, oroutdoors demand far greaterdegrees ofenvironmental robustness. There are several different ways of building acoustical robustness into speech recognition systems. Arrays of microphones can be used to develop a directionally-sensitive system that resists intelference from competing talkers and other noise sources that are spatially separated from the source of the desired speech signal.
This book is a description of some of the most recent advances in text classification as part of a concerted effort to achieve computer understanding of human language. In particular, it addresses state-of-the-art developments in the computation of higher-level linguistic features, ranging from etymology to grammar and syntax for the practical task of text classification according to genres, registers and subject domains. Serving as a bridge between computational methods and sophisticated linguistic analysis, this book will be of particular interest to academics and students of computational linguistics as well as professionals in natural language engineering.
Waibel, (computer science, Carnegie-Mellon U.), focuses on the prosodic cues (e.g., pitch, intensity, rhythm, temporal relationships, stress) that are critical to human speech perception. No index. Annotation copyrighted by Book News, Inc., Portland, OR
This book integrates a wide range of research topics related to and necessary for the development of proactive, smart, computers in the human interaction loop, including the development of audio-visual perceptual components for such environments; the design, implementation and analysis of novel proactive perceptive services supporting humans; the development of software architectures, ontologies and tools necessary for building such environments and services, as well as approaches for the evaluation of such technologies and services. The book is based on a major European Integrated Project, CHLI (Computers in the Human Interaction Loop), and throws light on the paradigm shift in the area of HCI that rather than humans interactive directly with machines, computers should observe and understand human interaction, and support humans during their work and interaction in an implicit and proactive manner.
Elect Alex is a short book offering a new interpretation of both the Bible and the Quran. Author Alex J. Clark Jr. emphasizes peace, love, and kindness as a solution to the many crimes he sees in the world around us.
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