Spoken language understanding (SLU) is an emerging field in between speech and language processing, investigating human/ machine and human/ human communication by leveraging technologies from signal processing, pattern recognition, machine learning and artificial intelligence. SLU systems are designed to extract the meaning from speech utterances and its applications are vast, from voice search in mobile devices to meeting summarization, attracting interest from both commercial and academic sectors.
Both human/machine and human/human communications can benefit from the application of SLU, using differing tasks and approaches to better understand and utilize such communications. This book covers the state-of-the-art approaches for the most popular SLU tasks with chapters written by well-known researchers in the respective fields. Key features include:
- Presents a fully integrated view of the two distinct disciplines of speech processing and language processing for SLU tasks.
- Defines what is possible today for SLU as an enabling technology for enterprise (e.g., customer care centers or company meetings), and consumer (e.g., entertainment, mobile, car, robot, or smart environments) applications and outlines the key research areas.
- Provides a unique source of distilled information on methods for computer modeling of semantic information in human/machine and human/human conversations.
This book can be successfully used for graduate courses in electronics engineering, computer science or computational linguistics. Moreover, technologists interested in processing spoken communications will find it a useful source of collated information of the topic drawn from the two distinct disciplines of speech processing and language processing under the new area of SLU.Content:
Chapter 1 Introduction (pages 1–7): Gokhan Tur and Renato De Mori
Chapter 2 History of Knowledge and Processes for Spoken Language Understanding (pages 9–40): Renato De Mori
Chapter 3 Semantic Frame?Based Spoken Language Understanding (pages 41–91): Ye?Yi Wang, Li Deng and Alex Acero
Chapter 4 Intent Determination and Spoken Utterance Classification (pages 93–118): Gokhan Tur and Li Deng
Chapter 5 Voice Search (pages 119–146): Ye?Yi Wang, Dong Yu, Yun?Cheng Ju and Alex Acero
Chapter 6 Spoken Question Answering (pages 147–170): Sophie Rosset, Olivier Galibert and Lori Lamel
Chapter 7 SLU in Commercial and Research Spoken Dialogue Systems (pages 171–194): David Suendermann and Roberto Pieraccini
Chapter 8 Active Learning (pages 195–224): Dilek Hakkani?Tur and Giuseppe Riccardi
Chapter 9 Human/Human Conversation Understanding (pages 225–255): Gokhan Tur and Dilek Hakkani?Tur
Chapter 10 Named Entity Recognition (pages 257–290): Frederic Bechet
Chapter 11 Topic Segmentation (pages 291–317): Matthew Purver
Chapter 12 Topic Identification (pages 319–356): Timothy J. Hazen
Chapter 13 Speech Summarization (pages 357–396): Yang Liu and Dilek Hakkani?Tur
Chapter 14 Speech Analytics (pages 397–416): I. Dan Melamed and Mazin Gilbert
Chapter 15 Speech Retrieval (pages 417–446): Ciprian Chelba, Timothy J. Hazen, Bhuvana Ramabhadran and Murat Saraclar