International Speech Communication Association (isca) Microsoft Research International Speech Communication Association (isca) Special Interest Group on Discourse and Dialogue (sigdial) Dialogs on Dialogs Student Reading Group Organizing Committee: Advisory Committee: Workshop Program 10:30 -12:00 M

Preface The design and study of spoken dialog systems is a relatively young research field compared to other speech technologies such as recognition and synthesis. In recent years however, as these core technologies have improved, the field of spoken dialog systems has been generating increased interest both in the research community and in the industry. While most of the early work originated from the artificial intelligence community and addressed high-level issues such as discourse planning, the development and deployment of actual usable systems has led to the emergence of a wide range of new issues such as error handling in dialog, multimodal integration, or rapid system development. At the same time, researchers from a variety of disciplines including speech and language technologies, robotics, and human-computer interaction have started to bring their unique skills and backgrounds to bear on these issues. Unfortunately, while this richness and variety of interests constitute a definite strength, they can also be a source of isolation and discouragement, particularly for newcomers to the field. Many young researchers in spoken dialog systems work within small research groups and find it difficult to share their ideas with peers having similar or complementary interests. While annual conferences such as SIGdial and Interspeech provide excellent opportunities for young researchers to present their own work and hear about work that is done in similar areas, there have been few opportunities to date for more intensive discussion and thought about interesting and challenging questions in the field today. We believe that both young researchers and the field itself would benefit greatly from a better communication across institutions and disciplines. By working together, getting peer-level feedback on their research, and engaging in brainstorming sessions, researchers could identify the questions that are most relevant to the overall problem of spoken human-machine communication, and come up with fresh ideas to answer these questions. With these goals in mind, in 2002 we started Dialogs on Dialogs (www.cs.cmu.edu/~dod), an international student reading group focused on the area of Spoken Dialog Systems/Conversational Agents. The group is based at Carnegie Mellon University and involves participants from other universities through teleconferencing. Our biweekly meetings provide a setting in which we can present our own research and obtain feedback from others who are at our level and who are working on similar problems. The Young Researchers' Roundtable on Spoken Dialog Systems workshop was conceived as an extension of these activities. The …

[1]  Victor Zue,et al.  Multilingual spoken-language understanding in the MIT Voyager system , 1995, Speech Commun..

[2]  P R Cohen,et al.  The role of voice input for human-machine communication. , 1995, Proceedings of the National Academy of Sciences of the United States of America.

[3]  Marilyn A. Walker,et al.  Evaluating Interactive Dialogue Systems: Extending Component Evaluation to Integrated System Evaluation , 1997, Real Applications@ACL/EACL.

[4]  Niels Ole Bernsen,et al.  Designing interactive speech systems - from first ideas to user testing , 1998 .

[5]  Alexander H. Waibel,et al.  Model-based and empirical evaluation of multimodal interactive error correction , 1999, CHI '99.

[6]  Daniel B. Horn,et al.  Patterns of entry and correction in large vocabulary continuous speech recognition systems , 1999, CHI '99.

[7]  Peter Regel-Brietzmann,et al.  Issues in the Evaluation of Spoken Dialogue Systems - Experience from the ACCeSS Project , 2000, LREC.

[8]  Alan F. Blackwell,et al.  Dasher—a data entry interface using continuous gestures and language models , 2000, UIST '00.

[9]  Clare-Marie Karat,et al.  Productivity, satisfaction, and interaction strategies of individuals with spinal cord injuries and traditional users interacting with speech recognition software , 2001, Universal Access in the Information Society.

[10]  Emiel Krahmer,et al.  Multi-feature Error Detection in Spoken Dialogue Systems , 2001, CLIN.

[11]  Carolyn Penstein Rosé,et al.  The Architecture of Why2-Atlas: A Coach for Qualitative Physics Essay Writing , 2002, Intelligent Tutoring Systems.

[12]  I. Scott MacKenzie,et al.  Text Entry for Mobile Computing: Models and Methods,Theory and Practice , 2002, Hum. Comput. Interact..

[13]  Emiel Krahmer,et al.  Improving machine-learned detection of miscommunications in human-machine dialogues through informed data splitting , 2002 .

[14]  Ronald Rosenfeld,et al.  Towards every-citizen²s speech interface: an application generator for speech interfaces to databases , 2002, INTERSPEECH.

[15]  Jason D. Williams,et al.  Evaluating real callers' reactions to Open and Directed Strategy prompts , 2003 .

[16]  Emiel Krahmer,et al.  Machine Learning for Shallow Interpretation of User Utterances in Spoken Dialogue Systems , 2003 .

[17]  Gabriel Skantze Exploring Human Error Handling Strategies : Implications for Spoken Dialogue Systems , 2003 .

[18]  Carolyn Penstein Rosé,et al.  A Hybrid Text Classification Approach for Analysis of Student Essays , 2003, HLT-NAACL 2003.

[19]  Piroska Lendvai,et al.  Process discovery for evaluating dialogue strategies , 2003 .

[20]  Jason D. Williams,et al.  Preference, perception, and task completion of open, menu-based, and directed prompts for call routing: a case study , 2003, INTERSPEECH.

[21]  Jason D. Williams,et al.  Two studies of open vs. directed dialog strategies in spoken dialog systems , 2003, INTERSPEECH.

[22]  Steve J. Young,et al.  Using Wizard-of-Oz simulations to bootstrap Reinforcement - Learning based dialog management systems , 2003, SIGDIAL Workshop.

[23]  Emiel Krahmer,et al.  Memory-based disfluency chunking , 2003, DiSS.

[24]  Steve Young,et al.  A Framework for Wizard-of-Oz Experiments with a Simulated ASR-Channel , 2004 .

[25]  Matthew Purver The Theory and Use of Clarification Requests in Dialogue , 2004 .

[26]  Stephanie Seneff,et al.  Error Detection and Recovery in Spoken Dialogue Systems , 2004, HLT-NAACL 2004.

[27]  Olivier Pietquin,et al.  A Framework for Unsupervised Learning of Dialogue Strategies , 2004 .

[28]  Jens Edlund,et al.  Early error detection on word level , 2004 .

[29]  Jens Edlund,et al.  Robust interpretation in the Higgins spoken dialogue system , 2004 .

[30]  Jason D. Williams,et al.  A Comparison of Dialog Strategies for Call Routing , 2004, Int. J. Speech Technol..

[31]  R. Swaminathan,et al.  Multilingual Speech Recognition for Information Retrieval in Indian Context , 2004, HLT-NAACL.

[32]  Verena Rieser Confidence-Based Fragmentary Clarifications on Several Levels for Robust Dialogue Systems , 2004 .

[33]  Steve J. Young,et al.  Characterizing task-oriented dialog using a simulated ASR chanel , 2004, INTERSPEECH.

[34]  Shumin Zhai,et al.  TNT: a numeric keypad based text input method , 2004, CHI '04.

[35]  David Schlangen,et al.  Causes and Strategies for Requesting Clarification in Dialogue , 2004, SIGDIAL Workshop.

[36]  Emiel Krahmer,et al.  Memory-based Robust Interpretation of Recognised Speech , 2004 .

[37]  Haizhou Li,et al.  Language identification for multilingual speech recognition systems , 2004 .

[38]  Ronald Rosenfeld,et al.  Speech Graffiti Habitability: What Do Users Really Say? , 2004, SIGDIAL Workshop.

[39]  Andrew Sears,et al.  Are we speaking slower than we type?: exploring the gap between natural speech, typing and speech-based dictation , 2004, ASAC.

[40]  Johanna D. Moore,et al.  Implications for Generating Clarification Requests in Task-Oriented Dialogues , 2005, ACL.

[41]  Gabriel Skantze,et al.  Exploring human error recovery strategies: Implications for spoken dialogue systems , 2005, Speech Communication.

[42]  Diane J. Litman,et al.  Dialogue-Learning Correlations in Spoken Dialogue Tutoring , 2005, AIED.

[43]  Piroska Lendvai,et al.  Robust ASR lattice representation types in pragma-semantic processing of spoken input , 2005 .

[44]  Diane J. Litman,et al.  Interactions between speech recognition problems and user emotions , 2005, INTERSPEECH.

[45]  Keith Vertanen Efficient Computer Interfaces Using Continuous Gestures, Language Models, and Speech , 2005 .

[46]  Diane J. Litman,et al.  Using word-level pitch features to better predict student emotions during spoken tutoring dialogues , 2005, INTERSPEECH.

[47]  Kallirroi Georgila,et al.  Quantitative Evaluation of User Simulation Techniques for Spoken Dialogue Systems , 2005, SIGDIAL.

[48]  Ben Shneiderman,et al.  Designing The User Interface , 2013 .