Crowdsourcing for Speech Processing: Applications to Data Collection, Transcription and Assessment

Provides an insightful and practical introduction to crowdsourcing as a means of rapidly processing speech data Intended for those who want to get started in the domain and  learn how to set up a task, what interfaces are available, how to assess the work, etc. as well as for those who already have used crowdsourcing and want to create better tasks and obtain better assessments of the work of the crowd. It will include screenshots to show examples of good and poor interfaces; examples of case studies in speech processing tasks, going through the task creation process, reviewing options in the interface, in the choice of medium (MTurk or other) and explaining choices, etc. Provides an insightful and practical introduction to crowdsourcing as a means of rapidly processing speech data. Addresses important aspects of this new technique that should be mastered before attempting a crowdsourcing application. Offers speech researchers the hope that they can spend much less time dealing with the data gathering/annotation bottleneck, leaving them to focus on the scientific issues.  Readers will directly benefit from the book’s successful examples of how crowd- sourcing was implemented for speech processing, discussions of interface and processing choices that worked and  choices that didn’t, and guidelines on how to play and record speech over the internet, how to design tasks, and how to assess workers. Essential reading for researchers and practitioners in speech research groups involved in speech processing

Table of Contents:
Contents List of Contributors xiii Preface xv 1 An Overview 1 Maxine Eskénazi 1.1 Origins of Crowdsourcing 2 1.2 Operational Definition of Crowdsourcing 3 1.3 Functional Definition of Crowdsourcing 3 1.4 Some Issues 4 1.5 Some Terminology 6 1.6 Acknowledgments 6 References 6 2 The Basics 8 Maxine Eskénazi 2.1 An Overview of the Literature on Crowdsourcing for Speech Processing 8 2.2 Alternative Solutions 14 2.3 Some Ready-Made Platforms for Crowdsourcing 15 2.4 Making Task Creation Easier 17 2.5 Getting Down to Brass Tacks 17 2.6 Quality Control 29 2.7 Judging the Quality of the Literature 32 2.8 Some Quick Tips 33 2.9 Acknowledgments 33 References 33 Further reading 35 3 Collecting Speech from Crowds 37 Ian McGraw 3.1 A Short History of Speech Collection 38 3.2 Technology for Web-Based Audio Collection 43 3.3 Example: WAMI Recorder 49 3.4 Example: The WAMI Server 52 3.5 Example: Speech Collection on Amazon Mechanical Turk 59 3.6 Using the Platform Purely for Payment 65 3.7 Advanced Methods of Crowdsourced Audio Collection 67 3.8 Summary 69 3.9 Acknowledgments 69 References 70 4 Crowdsourcing for Speech Transcription 72 Gabriel Parent 4.1 Introduction 72 4.2 Transcribing Speech 73 4.3 Preparing the Data 80 4.4 Setting Up the Task 83 4.5 Submitting the Open Call 91 4.6 Quality Control 95 4.7 Conclusion 102 4.8 Acknowledgments 103 References 103 5 How to Control and Utilize Crowd-Collected Speech 106 Ian McGraw and Joseph Polifroni 5.1 Read Speech 107 5.2 Multimodal Dialog Interactions 111 5.3 Games for Speech Collection 120 5.4 Quizlet 121 5.5 Voice Race 123 5.6 Voice Scatter 129 5.7 Summary 135 5.8 Acknowledgments 135 References 136 6 Crowdsourcing in Speech Perception 137 Martin Cooke, Jon Barker, and Maria Luisa Garcia Lecumberri 6.1 Introduction 137 6.2 Previous Use of Crowdsourcing in Speech and Hearing 138 6.3 Challenges 140 6.4 Tasks 145 6.5 BigListen: A Case Study in the Use of Crowdsourcing to Identify Words in Noise 149 6.6 Issues for Further Exploration 167 6.7 Conclusions 169 References 169 7 Crowdsourced Assessment of Speech Synthesis 173 Sabine Buchholz, Javier Latorre, and Kayoko Yanagisawa 7.1 Introduction 173 7.2 Human Assessment of TTS 174 7.3 Crowdsourcing for TTS: What Worked and What Did Not 177 7.4 Related Work: Detecting and Preventing Spamming 193 7.5 Our Experiences: Detecting and Preventing Spamming 195 7.6 Conclusions and Discussion 212 References 214 8 Crowdsourcing for Spoken Dialog System Evaluation 217 Zhaojun Yang, Gina-Anne Levow, and Helen Meng 8.1 Introduction 217 8.2 Prior Work on Crowdsourcing: Dialog and Speech Assessment 220 8.3 Prior Work in SDS Evaluation 221 8.4 Experimental Corpus and Automatic Dialog Classification 225 8.5 Collecting User Judgments on Spoken Dialogs with Crowdsourcing 226 8.6 Collected Data and Analysis 230 8.7 Conclusions and Future Work 238 8.8 Acknowledgments 238 References 239 9 Interfaces for Crowdsourcing Platforms 241 Christoph Draxler 9.1 Introduction 241 9.2 Technology 242 9.3 Crowdsourcing Platforms 253 9.4 Interfaces to Crowdsourcing Platforms 261 9.5 Summary 278 References 278 10 Crowdsourcing for Industrial Spoken Dialog Systems 280 David Suendermann and Roberto Pieraccini 10.1 Introduction 280 10.2 Architecture 283 10.3 Transcription 287 10.4 Semantic Annotation 290 10.5 Subjective Evaluation of Spoken Dialog Systems 296 10.6 Conclusion 300 References 300 11 Economic and Ethical Background of Crowdsourcing for Speech 303 Gilles Adda, Joseph J. Mariani, Laurent Besacier, and Hadrien Gelas 11.1 Introduction 303 11.2 The Crowdsourcing Fauna 304 11.3 Economic and Ethical Issues 307 11.4 Under-Resourced Languages: A Case Study 316 11.5 Toward Ethically Produced Language Resources 322 11.6 Conclusion 330 Disclaimer 331 References 331 Index 335

About the Author :
Maxine Eskenazi, Carnegie Mellon University, USA Dr. Eskenazi is Principal Systems Scientist at the Language Technologies Institute, Carnegie Mellon University, USA. She has authored over 100 scientific papers in the areas of computer assisted language learning and speech and spoken dialog systems. Her work has produced such systems as the Let's Go spoken dialog system and the REAP vocabulary tutor. She is also the founder and CTO of the Carnegie Speech Company. Gina-Anne Levow, University of Washington, USA Dr. Levow is currently an Assistant Professor in the Department of Linguistics, University of Washington, USA. Prior to joining the faculty at the University of Washington, she served on the faculty at the University of Chicago in the Department of Computer Science and as a Research Fellow at the University of Manchester, UK. She served on the Editorial Board of Computational Linguistics and as Associate Editor of ACM Transactions on Asian Language Processing. Helen Meng, The Chinese University of Hong Kong, Hong Kong Dr. Meng is Founder and Director of the Human-Computer Communications Laboratory at The Chinese University of Hong Kong, and is also the Founder and Co-Director of the Microsoft-CUHK Joint Laboratory for Human-Centric Computing and Interface Technologies, which was conferred the national status of the Ministry of Education of China (MoE) Key Laboratory in 2008. Prof. Meng also served as an Associate Dean (Research) of the Faculty of Engineering from 2006 to 2010. She serves as Editor-in-Chief of the IEEE Transactions on Audio, Speech and Language Processing. Gabriel Parent, Amazon.com, USA Gabriel Parent is a Software Development Engineer at Amazon.com working on solving natural language related problems. His main research focuses were human-computer interaction through spoken dialog systems and crowdsourcing. David Suendermann, Baden-Wuerttemberg Cooperative State University, Germany Dr. Sundermann is currently full Professor of Computer Science at the Baden-Wuerttemberg Cooperative State University, Stuttgart, Germany. He is also the Principal Speech Scientist of SpeechCycle, New York, USA which has been recognized by Deloitte as a "Technology Fast 500" company based on revenue growth. He has authored more than 70 publications and patents, including a book and six book chapters.