Spoken Language Understanding Systems for Extracting Semantic Information from Speech

by Tur, Gokhan; De Mori, Renato

• ISBN13: 9780470688243
• ISBN10: 0470688246
• Edition: 1st
• Format: Hardcover
• Copyright: 2011-04-25
• Publisher: Wiley

Summary

A state of the art exploration of spoken language understanding, an active research field combining speech processing and language processing Historically, speech processing and language processing communities came from different backgrounds, and current literature reflects this structure; there is very little that brings the two disciplines together to focus on spoken language understanding. Its interdisciplinary nature means it lacks the common ground for establishing benchmarks or having a single platform to communicate, unlike speech or natural language processing. This book is the first to focus exclusively on this growing topic. Authorities in SLU from both academia and research contribute state of the art research and approaches. Companion website hosting continually updated material, software and data sources. Includes a strong datadriven, statistical approach. A book with relevance to those working on industry/commercial applications of SLU, such as within call centres, robotics, and some smart environments such as cars.

Author Biography

Gokhan Tur, Microsoft Research, California, USA
Dr Tur is currently a Principal Scientist in the Speech at Microsoft Department at Microsoft Research, Mountain View, California, USA. He was formerly a Research Scientist at SRI International which is an independent, nonprofit research institute conducting client-sponsored research and development for government agencies, commercial businesses, foundations, and other organizations. He has co-authored more than 70 journal and conference papers. Dr. Tur was the recipient of the Speech Communication Journal Best Paper awards by ISCA for 2004-2006 and by EURASIP for 2005-2006. He is a senior member of IEEE, ACL, and ISCA, and a member of IEEE Signal Processing Society (SPS), and Speech and Language Technical Committee (SLTC) for 2006-2008. He was a guest editor of Speech Communication (Elsevier) for a special issue on Spoken Language Understanding (SLU). He has been involved with organising various conferences and is the spoken language processing area chair for ICASSP 2009.

Renato De Mori, University of Avignon, France
Dr De Mori is a Professor of Computer Science at the Université d'Avignon, as well as Director of its Laboratoire d'Informatique and is a Visiting Professor at McGill University, Canada. He is a Fellow of the Computer Society of the IEEE and a Distinguished Lecturer of the IEEE Signal Processing Society. He is the author or editor of four books and has published more than 100 scientific papers in many international journals. Professor De Mori has been a member of the Executive Advisory Board at the IBM Toronto Lab, Scientific Advisor at France Télécom R&D, Chairman of the Computer and Information Systems Committee, Natural Sciences and Engineering Council of Canada, Vice-President R&D, Centre de Recherche en Informatique de Montréal.

Table of Contents

List of Contributors.

Forward.

Preface.

1 Introduction (Gokhan Tur and Renato De Mori).

1.1 A Brief History of Spoken Language Understanding.

1.2 Organization of the Book.

PART 1 SPOKEN LANGUAGE UNDERSTANDING FOR HUMAN/MACHINE INTERACTIONS.

2 History of Knowledge and Processes for Spoken Language Understanding (Renato De Mori).

2.1 Introduction.

2.2 Meaning Representation and Sentence Interpretation.

2.3 Knowledge Fragments and Semantic Composition.

2.4 Probabilistic Interpretation in SLU Systems.

2.5 Interpretation with Partial Syntactic Analysis.

2.6 Classification Models for Interpretation.

2.7 Advanced Methods and Resources for Semantic Modeling and Interpretation.

2.8 Recent Systems.

2.9 Conclusions.

References.

3 Semantic Frame-based Spoken Language Understanding (Ye-Yi Wang, Li Deng and Alex Acero).

3.1 Background.

3.2 Knowledge-based Solutions.

3.3 Data-driven Approaches.

3.4 Summary.

References.

4 Intent Determination and Spoken Utterance Classification (Gokhan Tur and Li Deng).

4.1 Background.

4.2 Task Description.

4.3 Technical Challenges.

4.4 Benchmark Data Sets.

4.5 Evaluation Metrics.

4.6 Technical Approaches.

4.7 Discussion and Conclusions.

References.

5 Voice Search (Ye-Yi Wang, Dong Yu, Yun-Cheng Ju and Alex Acero).

5.1 Background.

5.2 Technology Review.

5.3 Summary.

References.

6 Spoken Question Answering (Sophie Rosset, Olivier Galibert and Lori Lamel).

6.1 Introduction.

6.2 Specific Aspects of Handling Speech in QA Systems.

6.3 QA Evaluation Campaigns.

6.4 Question-answering Systems.

6.5 Projects Integrating Spoken Requests and Question Answering.

6.6 Conclusions.

References.

7 SLU in Commercial and Research Spoken Dialogue Systems (David Suendermann and Roberto Pieraccini).

7.1 Why Spoken Dialogue Systems (Do Not) Have to Understand.

7.2 Approaches to SLU for Dialogue Systems.

7.3 From Call Flow to POMDP: How Dialogue Management Integrates with SLU.

7.4 Benchmark Projects and Data Sets.

7.5 Time is Money: The Relationship between SLU and Overall Dialogue System Performance.

7.6 Conclusion.

References.

8 Active Learning (Dilek Hakkani-Tür and Giuseppe Riccardi).

8.1 Introduction.

8.2 Motivation.

8.3 Learning Architectures.

8.4 Active Learning Methods.

8.5 Combining Active Learning with Semi-supervised Learning.

8.6 Applications.

8.7 Evaluation of Active Learning Methods.

8.8 Discussion and Conclusions.

References.

PART 2 SPOKEN LANGUAGE UNDERSTANDING FOR HUMAN/HUMAN CONVERSATIONS.

9 Human/Human Conversation Understanding (Gokhan Tur and Dilek Hakkani-Tür).

9.1 Background.

9.2 Human/Human Conversation Understanding Tasks.

9.3 Dialogue Act Segmentation and Tagging.

9.4 Action Item and Decision Detection.

9.5 Addressee Detection and Co-reference Resolution.

9.6 Hot Spot Detection.

9.7 Subjectivity, Sentiment, and Opinion Detection.

9.8 Speaker Role Detection.

9.9 Modeling Dominance.

9.10 Argument Diagramming.

9.11 Discussion and Conclusions.

References.

10 Named Entity Recognition (Frédéric Béchet).

10.1 Task Description.

10.2 Challenges Using Speech Input.

10.3 Benchmark Data Sets, Applications.

10.4 Evaluation Metrics.

10.5 Main Approaches for Extracting NEs from Text.

10.6 Comparative Methods for NER from Speech.

10.7 New Trends in NER from Speech.

10.8 Conclusions.

References.

11 Topic Segmentation (Matthew Purver).

11.1 Task Description.

11.2 Basic Approaches, and the Challenge of Speech.

11.3 Applications and Benchmark Datasets.

11.4 Evaluation Metrics.

11.5 Technical Approaches.

11.6 New Trends and Future Directions.

References.

12 Topic Identification (Timothy J. Hazen).

12.1 Task Description.

12.2 Challenges Using Speech Input.

12.3 Applications and Benchmark Tasks.

12.4 Evaluation Metrics.

12.5 Technical Approaches.

12.6 New Trends and Future Directions.

References.

13 Speech Summarization (Yang Liu and Dilek Hakkani-Tür).

13.1 Task Description.

13.2 Challenges when Using Speech Input.

13.3 Data Sets.

13.4 Evaluation Metrics.

13.5 General Approaches.

13.6 More Discussions on Speech versus Text Summarization.

13.7 Conclusions.

References.

14 Speech Analytics (I. Dan Melamed and Mazin Gilbert)

14.1 Introduction.

14.2 System Architecture.

14.3 Speech Transcription.

14.4 Text Feature Extraction.

14.5 Acoustic Feature Extraction.

14.6 Relational Feature Extraction.

14.7 DBMS.

14.8 Media Server and Player.

14.9 Trend Analysis.

14.10 Alerting System.

14.11 Conclusion.

References.

15 Speech Retrieval (Ciprian Chelba, Timothy J. Hazen, Bhuvana Ramabhadran and Murat Saraçlar).

15.1 Task Description.

15.2 Applications.

15.3 Challenges Using Speech Input.

15.4 Evaluation Metrics.

15.5 Benchmark Data Sets.

15.6 Approaches.

15.7 New Trends.

15.8 Discussion and Conclusions.

References.

Index.