Nitendra Rajput, Amit Anil Nanavati

Speech in Mobile and Pervasive Environments (eBook, PDF)

• Format: PDF

Produktbeschreibung

This book provides a cross-disciplinary reference to speech in mobile and pervasive environments Speech in Mobile and Pervasive Environments addresses the issues related to speech processing on resource-constrained mobile devices. These include speech recognition in noisy environments, specialised hardware for speech recognition and synthesis, the use of context to enhance recognition and user experience, and the emerging software standards required for interoperability. This book takes a multi-disciplinary look at these matters, while offering an insight into the opportunities and challenges of speech processing in mobile environs. In developing regions, speech-on-mobile is set to play a momentous role, socially and economically; the authors discuss how voice-based solutions and applications offer a compelling and natural solution in this setting. Key Features * Provides a holistic overview of all speech technology related topics in the context of mobility * Brings together the latest research in a logically connected way in a single volume * Covers hardware, embedded recognition and synthesis, distributed speech recognition, software technologies, contextual interfaces * Discusses multimodal dialogue systems and their evaluation * Introduces speech in mobile and pervasive environments for developing regions This book provides a comprehensive overview for beginners and experts alike. It can be used as a textbook for advanced undergraduate and postgraduate students in electrical engineering and computer science. Students, practitioners or researchers in the areas of mobile computing, speech processing, voice applications, human-computer interfaces, and information and communication technologies will also find this reference insightful. For experts in the above domains, this book complements their strengths. In addition, the book will serve as a guide to practitioners working in telecom-related industries.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 312
• Erscheinungstermin: 26. Januar 2012
• Englisch
• ISBN-13: 9781119961703
• Artikelnr.: 37339197

Autorenporträt

Mr Nitendra Rajput, IBM Research, New Delhi, India Nitendra Rajput is a Research Staff Member with IBM India Reseach Lab (IRL) in New Delhi since 1998. Prior to this, he finished his Masters from Indian Institute of Technology, Bombay in Communications. At IRL, he has been working in the field of conversational systems for the last nine years. He has worked on Audio Visual Speech recognition, speech recognition systems for Indian languages. His interests are in statistical signal processing, dialog management, speech and image processing. Mr Amit A. Nanavati, IBM Research, New Delhi, India Amit A. Nanavati is a Research Staff Member, working in the Telecom Research Innovation Centre at IBM India Research Lab. For the last four years, he has been actively working in the area of mobile and pervasive computing. He was involved with the MDAT (Multi-device Authoring Technology) project (now a product) for adapting applications to pervasive devices. His research interests include information retrieval and constructing models for evaluation. Prior to joining IBM, he was working with Netscape for 4 years.

Inhaltsangabe

About the Series Editors xiii List of Contributors xv Foreword xvii Preface
xix Acknowledgments xxiii 1 Introduction 1 1.1 Application design 3 1.2
Interaction modality 3 1.3 Speech processing 4 1.4 Evaluations 5 2 Mobile
Speech Hardware: The Case for Custom Silicon 7 2.1 Introduction 7 2.2
Mobile hardware: Capabilities and limitations 11 2.2.1 Looking inside a
mobile device: Smartphone example 11 2.2.2 Processing limitations 14 2.2.3
Memory limitations 16 2.2.4 Power limitations 19 2.2.5 Silicon technology
and mobile hardware 22 2.3 Profiling existing software systems 24 2.3.1
Speech recognition overview 24 2.3.2 Profiling techniques summary 25 2.3.3
Processing time breakdown 27 2.3.4 Memory usage 29 2.3.5 Power and energy
breakdown 30 2.3.6 Summary 32 2.4 Recognizers for mobile hardware:
Conventional approaches 32 2.4.1 Reduced-resource embedded recognizers 33
2.4.2 Network recognizers 35 2.4.3 Distributed recognizers 36 2.4.4 An
alternative approach: Custom hardware 38 2.5 Custom hardware for mobile
speech recognition 38 2.5.1 Motivation 38 2.5.2 Hardware implementation:
Feature extraction 40 2.5.3 Hardware implementation: Feature scoring 41
2.5.4 Hardware implementation: Search 44 2.5.5 Hardware implementation:
Performance and power evaluation 47 2.5.6 Hardware implementation: Summary
49 2.6 Conclusion 49 Bibliography 50 3 Embedded Automatic Speech
Recognition and Text-to-Speech Synthesis 57 3.1 Automatic speech
recognition 57 3.2 Mathematical formulation 58 3.3 Acoustic
parameterization 60 3.3.1 Landmark-based approach 64 3.4 Acoustic modeling
64 3.4.1 Unit selection 64 3.4.2 Hidden Markov models 66 3.5 Language
modeling 69 3.6 Modifications for embedded speech recognition 71 3.6.1
Feature computation 71 3.6.2 Likelihood computation 75 3.7 Applications 77
3.7.1 Car navigation systems 77 3.7.2 Smart homes 78 3.7.3 Interactive toys
78 3.7.4 Smartphones 79 3.8 Text-to-speech synthesis 79 3.9 Text to speech
in a nutshell 80 3.10 Front end 81 3.11 Back end 84 3.11.1 Rule-based
synthesis 84 3.11.2 Data-driven synthesis 86 3.11.3 Statistical parameteric
speech synthesis 90 3.12 Embedded text-to-speech 91 3.13 Evaluation 92 3.14
Summary 94 Bibliography 94 4 Distributed Speech Recognition 99 4.1 Elements
of distributed speech processing 100 4.2 Front-end processing 101 4.2.1
Device requirements 103 4.2.2 Transmission issues in DSR 104 4.2.3 Back-end
processing 105 4.3 ETSI standards 106 4.3.1 Basic front-end standard ES 201
108 107 4.3.2 Noise-robust front-end standard ES 202 050 107 4.3.3
Tonal-language recognition standard ES 202 211 107 4.4 Transfer protocol
108 4.4.1 Signaling 109 4.4.2 RTP payload format 109 4.5 Energy-aware
distributed speech recognition 110 4.6 ESR, NSR, DSR 111 Bibliography 113 5
Context in Conversation 115 5.1 Context modeling and aggregation 115 5.1.1
An example of composer specification 121 5.2 Context-based speech
applications: Conspeakuous 122 5.2.1 Conspeakuous architecture 124 5.2.2
B-Conspeakuous 125 5.2.3 Learning as a source of context 125 5.2.4
Implementation 127 5.2.5 A tourist portal application 130 5.3 Context-based
speech applications: Responsive information architect 132 5.4 Conclusion
133 Bibliography 134 6 Software: Infrastructure, Standards, Technologies
137 6.1 Introduction 137 6.2 Mobile operating systems 139 6.3 Voice over
internet protocol 140 6.3.1 Implications for mobile speech 141 6.3.2 Sample
speech applications 142 6.3.3 Access channels 142 6.4 Standards 143 6.5
Standards: VXML 144 6.6 Standards: VoiceFleXML 145 6.6.1 Brief overview of
speech-based systems 147 6.6.2 System architecture 148 6.6.3 System
architecture: VoiceFleXML interpreter 150 6.6.4 VoiceFleXML: Voice browser
155 6.6.5 A prototype implementation 159 6.7 SAMVAAD 163 6.7.1 Background
and problem setting 165 6.7.2 Reorganization algorithms 166 6.7.3
Minimizing the number of dialogs 167 6.7.4 Hybrid call-flows 171 6.7.5
Minimally altered call-flows 172 6.7.6 Device-independent call-flow
characterization 174 6.7.7 SAMVAAD: Architecture, implementation and
experiments 175 6.7.8 Splitting dialog call-flows 180 6.8 Conclusion 188
6.9 Summary and future work 188 Bibliography 189 7 Architecture of Mobile
Speech-Based and Multimodal Dialog Systems 191 7.1 Introduction 191 7.2
Multimodal architectures 193 7.3 Multimodal frameworks 195 7.4 Multimodal
mobile applications 196 7.4.1 Mobile companion 197 7.4.2 MUMS 199 7.4.3
TravelMan 200 7.4.4 Stopman 203 7.5 Architectural models 206 7.5.1
Client-server systems 207 7.5.2 Dialog description systems 208 7.5.3
Generic model for distributed mobile multimodal speech systems 210 7.6
Distribution in the Stopman system 211 7.7 Conclusions 214 Bibliography 214
8 Evaluation of Mobile and Pervasive Speech Applications 219 8.1
Introduction 220 8.1.1 Spoken interaction 220 8.1.2 Mobile-use context 222
8.1.3 Speech and mobility 223 8.2 Evaluation of mobile speech-based systems
224 8.2.1 User interface evaluation methodology 225 8.2.2 Technical
evaluation of speech-based systems 226 8.2.3 Usability evaluations 227
8.2.4 Subjective metrics and objective metrics 228 8.2.5 Laboratory and
field studies 230 8.2.6 Simulating mobility in the laboratory 231 8.2.7
Studying social context 232 8.2.8 Long- and short-term studies 232 8.2.9
Validity 233 8.3 Case studies 235 8.3.1 STOPMAN evaluation 235 8.3.2
TravelMan evaluation 240 8.3.3 Discussion 247 8.4 Theoretical measures for
dialog call-flows 248 8.4.1 Introduction 248 8.4.2 Dialog call-flow
characterization 250 8.4.3 (m,q,a)-characterization 251 8.4.4
(m,q,a)-complexity 253 8.4.5 Call-flow analysis using (m,q,a)-complexity
254 8.5 Conclusions 257 Bibliography 258 9 Developing Regions 263 9.1
Introduction 264 9.2 Applications and studies 264 9.2.1 VoiKiosk 265 9.2.2
HealthLine 267 9.2.3 The spoken web 268 9.2.4 TapBack 271 9.3 Systems 275
9.4 Challenges 278 Bibliography 278 Index 281