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A key concern of researchers involved in the creation and sharing of language resources is to attain maximum usability, reliability and longevity of these resources for present and future researchers in the language sciences. The view developed in this volume is that spoken corpora construction and sharing are major research endeavours that should also be laid open to academic debate in a manner that is more visible than is currently the case in corpus linguistics. The present volume brings together multiple research perspectives to bear on the question of what constitutes best practices for the construction of spoken corpora. The book brings into closer contact scholars whose specializations have often remained in relatively different streams of scientific investigation; that is, scholars whose work falls primarily in conversation analysis, pragmatics and discourse analysis, but who are involved in spoken corpus compilation, on the one hand, and scholars who also specialize in linguistics but who have been intensively involved in developing various infrastructures for spoken corpora, on the other hand. This combination of scholars brings into better relief the concerns of data providers, data curators and data users in linguistic research. This book is thus unique in that it highlights best practices from both the perspective of assembling, annotating and linguistic analysis of spoken corpora, as well as from the perspective of processing, archiving and disseminating spoken language. In doing so, the contributions emphasise not only the considerable promise that the rapid technological changes that society continues to experience in this area offer, but also possible dangers for the unwary.
Reasoning for Information: Seeking and Planning Dialogues provides a logic-based reasoning component for spoken language dialogue systems. This component, called Problem Assistant is responsible for processing constraints on a possible solution obtained from various sources, namely user and the system's domain-specific information. The authors also present findings on the implementation of a dialogue management interface to the Problem Assistant. The dialogue system supports simple mixed-initiative planning interactions in the TRAINS domain, which is still a relatively complex domain involving a number of logical constraints and relations forming the basis for the collaborative problem-solving behavior that drives the dialogue.
Speech is the most natural mode of communication and yet attempts to build systems which support robust habitable conversations between a human and a machine have so far had only limited success. A key reason is that current systems treat speech input as equivalent to a keyboard or mouse, and behaviour is controlled by predefined scripts that try to anticipate what the user will say and act accordingly. But speech recognisers make many errors and humans are not predictable; the result is systems which are difficult to design and fragile in use. Statistical methods for spoken dialogue management takes a radically different view. It treats dialogue as the problem of inferring a user's intentions based on what is said. The dialogue is modelled as a probabilistic network and the input speech acts are observations that provide evidence for performing Bayesian inference. The result is a system which is much more robust to speech recognition errors and for which a dialogue strategy can be learned automatically using reinforcement learning. The thesis describes both the architecture, the algorithms needed for fast real-time inference over very large networks, model parameter estimation and policy optimisation. This ground-breaking work will be of interest both to practitioners in spoken dialogue systems and to cognitive scientists interested in models of human behaviour.
This book provides insights into how deep learning techniques impact language and speech processing applications. The authors discuss the promise, limits and the new challenges in deep learning. The book covers the major differences between the various applications of deep learning and the classical machine learning techniques. The main objective of the book is to present a comprehensive survey of the major applications and research oriented articles based on deep learning techniques that are focused on natural language and speech signal processing. The book is relevant to academicians, research scholars, industrial experts, scientists and post graduate students working in the field of speech signal and natural language processing and would like to add deep learning to enhance capabilities of their work. Discusses current research challenges and future perspective about how deep learning techniques can be applied to improve NLP and speech processing applications; Presents and escalates the research trends and future direction of language and speech processing; Includes theoretical research, experimental results, and applications of deep learning.

DOD Pam

United States. Office of Armed Forces Information and Education

Published:

Total Pages: 140


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