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.

This book constitutes the strictly refereed post-workshop documentation of the ECAI'96 Workshop on Dialogue Processing in Spoken Language Systems, held in Budapest, Hungary, in August 1996, during ECAI'96. The volume presents 16 revised full papers including a detailed introduction and survey paper by the volume editors. The papers are organized in sections on foundations of spoken language dialogue systems, dialogue systems and prosodic aspects of spoken dialogue processing, spoken dialogue systems-design and implementation, and evaluation of systems. The book reports on work being pursued both in academia and in industry as a crucial issue in speech processing.

Dictation systems, read-aloud software for the blind, speech control of machinery, geographical information systems with speech input and output, and educational software with `talking head' artificial tutorial agents are already on the market. The field is expanding rapidly, and new methods and applications emerge almost daily. But good sources of systematic information have not kept pace with the body of information needed for development and evaluation of these systems. Much of this information is widely scattered through speech and acoustic engineering, linguistics, phonetics, and experimental psychology. The Handbook of Multimodal and Spoken Dialogue Systems presents current and developing best practice in resource creation for speech input/output software and hardware. This volume brings experts in these fields together to give detailed `how to' information and recommendations on planning spoken dialogue systems, designing and evaluating audiovisual and multimodal systems, and evaluating consumer off-the-shelf products. In addition to standard terminology in the field, the following topics are covered in depth: How to collect high quality data for designing, training, and evaluating multimodal and speech dialogue systems; How to evaluate real-life computer systems with speech input and output; How to describe and model human-computer dialogue precisely and in depth. Also included: The first systematic medium-scale compendium of terminology with definitions. This handbook has been especially designed for the needs of development engineers, decision-makers, researchers, and advanced level students in the fields of speech technology, multimodal interfaces, multimedia, computational linguistics, and phonetics.

Considerable progress has been made in recent years in the development of dialogue systems that support robust and efficient human-machine interaction using spoken language. Spoken dialogue technology allows various interactive applications to be built and used for practical purposes, and research focuses on issues that aim to increase the system's communicative competence by including aspects of error correction, cooperation, multimodality, and adaptation in context. This book gives a comprehensive view of state-of-the-art techniques that are used to build spoken dialogue systems. It provides an overview of the basic issues such as system architectures, various dialogue management methods, system evaluation, and also surveys advanced topics concerning extensions of the basic model to more conversational setups. The goal of the book is to provide an introduction to the methods, problems, and solutions that are used in dialogue system development and evaluation. It presents dialogue modelling and system development issues relevant in both academic and industrial environments and also discusses requirements and challenges for advanced interaction management and future research. Table of Contents: Preface / Introduction to Spoken Dialogue Systems / Dialogue Management / Error Handling / Case Studies: Advanced Approaches to Dialogue Management / Advanced Issues / Methodologies and Practices of Evaluation / Future Directions / References / Author Biographies

Spoken Dialogue Technology provides extensive coverage of spoken dialogue systems, ranging from the theoretical underpinnings of the study of dialogue through to a detailed look at a number of well-established methods and tools for developing spoken dialogue systems. The book enables students and practitioners to design and test dialogue systems using several available development environments and languages, including the CSLU toolkit, VoiceXML, SALT, and XHTML+ voice. This practical orientation is usually available otherwise only in reference manuals supplied with software development kits. The latest research in spoken dialogue systems is presented along with extensive coverage of the most relevant theoretical issues and a critical evaluation of current research prototypes. A dedicated web site containing supplementary materials, code, links to resources will enable readers to develop and test their own systems (). Previously such materials have been difficult to track down, available only on a range of disparate web sites and this web site provides a unique and useful reference source which will prove invaluable.

Human conversational partners are able, at least to a certain extent, to detect the speaker’s or listener’s emotional state and may attempt to respond to it accordingly. When instead one of the interlocutors is a computer a number of questions arise, such as the following: To what extent are dialogue systems able to simulate such behaviors? Can we learn the mechanisms of emotional be- viors from observing and analyzing the behavior of human speakers? How can emotionsbeautomaticallyrecognizedfromauser’smimics,gesturesandspeech? What possibilities does a dialogue system have to express emotions itself? And, very importantly, would emotional system behavior be desirable at all? Given the state of ongoing research into incorporating emotions in dialogue systems we found it timely to organize a Tutorial and Research Workshop on A?ectiveDialogueSystems(ADS2004)atKlosterIrseein GermanyduringJune 14–16, 2004. After two successful ISCA Tutorial and Research Workshops on Multimodal Dialogue Systems at the same location in 1999 and 2002, we felt that a workshop focusing on the role of a?ect in dialogue would be a valuable continuation of the workshop series. Due to its interdisciplinary nature, the workshop attracted submissions from researchers with very di?erent backgrounds and from many di?erent research areas, working on, for example, dialogue processing, speech recognition, speech synthesis, embodied conversational agents, computer graphics, animation, user modelling, tutoring systems, cognitive systems, and human-computer inter- tion.

Data driven methods have long been used in Automatic Speech Recognition (ASR) and Text-To-Speech (TTS) synthesis and have more recently been introduced for dialogue management, spoken language understanding, and Natural Language Generation. Machine learning is now present “end-to-end” in Spoken Dialogue Systems (SDS). However, these techniques require data collection and annotation campaigns, which can be time-consuming and expensive, as well as dataset expansion by simulation. In this book, we provide an overview of the current state of the field and of recent advances, with a specific focus on adaptivity.