{"product_id":"spoken-language-understanding-systems-for-extracting-semantic-information-from-speech-hardback-9780470688243","title":"Spoken Language Understanding; Systems for Extracting Semantic Information from Speech (Hardback) 9780470688243","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eSpoken Language Understanding\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eSystems for Extracting Semantic Information from Speech\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eGokhan Tur (Author), Renato De Mori (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9780470688243, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 24 March 2011\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e480 pages\u003cbr\u003e25.1 x 17.4 x 3.1 cm, 0.948 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e“The book also contains references to existing datasets that can be used by researchers interested in the field; these, together with the presented baseline, equip one with the necessary tools to step into this very daring and fascinating domain.”  (\u003ci\u003eZentralblatt MATH\u003c\/i\u003e, 2012)\u003c\/p\u003e \u003cbr\u003e \u003cbr\u003e\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eSpoken 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.  \u003cp\u003eBoth 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:\u003c\/p\u003e \u003cul\u003e \u003cli\u003ePresents a fully integrated view of the two distinct disciplines of speech processing and language processing for SLU tasks.\u003c\/li\u003e \u003cli\u003eDefines 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.\u003c\/li\u003e \u003cli\u003eProvides a unique source of distilled information on methods for computer modeling of semantic information in human\/machine and human\/human conversations.\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThis 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.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cb\u003eList of Contributors.\u003c\/b\u003e  \u003cp\u003e\u003cb\u003eForward.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePreface.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction\u003c\/b\u003e (\u003ci\u003eGokhan Tur and Renato De Mori\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e1.1 A Brief History of Spoken Language Understanding.\u003c\/p\u003e \u003cp\u003e1.2 Organization of the Book.\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART 1 SPOKEN LANGUAGE UNDERSTANDING FOR HUMAN\/MACHINE INTERACTIONS.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 History of Knowledge and Processes for Spoken Language Understanding\u003c\/b\u003e (\u003ci\u003eRenato De Mori\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e2.1 Introduction.\u003c\/p\u003e \u003cp\u003e2.2 Meaning Representation and Sentence Interpretation.\u003c\/p\u003e \u003cp\u003e2.3 Knowledge Fragments and Semantic Composition.\u003c\/p\u003e \u003cp\u003e2.4 Probabilistic Interpretation in SLU Systems.\u003c\/p\u003e \u003cp\u003e2.5 Interpretation with Partial Syntactic Analysis.\u003c\/p\u003e \u003cp\u003e2.6 Classification Models for Interpretation.\u003c\/p\u003e \u003cp\u003e2.7 Advanced Methods and Resources for Semantic Modeling and Interpretation.\u003c\/p\u003e \u003cp\u003e2.8 Recent Systems.\u003c\/p\u003e \u003cp\u003e2.9 Conclusions.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Semantic Frame-based Spoken Language Understanding\u003c\/b\u003e (\u003ci\u003eYe-Yi Wang, Li Deng and Alex Acero\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e3.1 Background.\u003c\/p\u003e \u003cp\u003e3.2 Knowledge-based Solutions.\u003c\/p\u003e \u003cp\u003e3.3 Data-driven Approaches.\u003c\/p\u003e \u003cp\u003e3.4 Summary.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Intent Determination and Spoken Utterance Classification\u003c\/b\u003e (\u003ci\u003eGokhan Tur and Li Deng\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e4.1 Background.\u003c\/p\u003e \u003cp\u003e4.2 Task Description.\u003c\/p\u003e \u003cp\u003e4.3 Technical Challenges.\u003c\/p\u003e \u003cp\u003e4.4 Benchmark Data Sets.\u003c\/p\u003e \u003cp\u003e4.5 Evaluation Metrics.\u003c\/p\u003e \u003cp\u003e4.6 Technical Approaches.\u003c\/p\u003e \u003cp\u003e4.7 Discussion and Conclusions.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Voice Search\u003c\/b\u003e (\u003ci\u003eYe-Yi Wang, Dong Yu, Yun-Cheng Ju and Alex Acero\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e5.1 Background.\u003c\/p\u003e \u003cp\u003e5.2 Technology Review.\u003c\/p\u003e \u003cp\u003e5.3 Summary.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Spoken Question Answering\u003c\/b\u003e (\u003ci\u003eSophie Rosset, Olivier Galibert and Lori Lamel\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e6.1 Introduction.\u003c\/p\u003e \u003cp\u003e6.2 Specific Aspects of Handling Speech in QA Systems.\u003c\/p\u003e \u003cp\u003e6.3 QA Evaluation Campaigns.\u003c\/p\u003e \u003cp\u003e6.4 Question-answering Systems.\u003c\/p\u003e \u003cp\u003e6.5 Projects Integrating Spoken Requests and Question Answering.\u003c\/p\u003e \u003cp\u003e6.6 Conclusions.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 SLU in Commercial and Research Spoken Dialogue Systems\u003c\/b\u003e (\u003ci\u003eDavid Suendermann and Roberto Pieraccini\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e7.1 Why Spoken Dialogue Systems (Do Not) Have to Understand.\u003c\/p\u003e \u003cp\u003e7.2 Approaches to SLU for Dialogue Systems.\u003c\/p\u003e \u003cp\u003e7.3 From Call Flow to POMDP: How Dialogue Management Integrates with SLU.\u003c\/p\u003e \u003cp\u003e7.4 Benchmark Projects and Data Sets.\u003c\/p\u003e \u003cp\u003e7.5 Time is Money: The Relationship between SLU and Overall Dialogue System Performance.\u003c\/p\u003e \u003cp\u003e7.6 Conclusion.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Active Learning\u003c\/b\u003e (\u003ci\u003eDilek Hakkani-Tür and Giuseppe Riccardi\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e8.1 Introduction.\u003c\/p\u003e \u003cp\u003e8.2 Motivation.\u003c\/p\u003e \u003cp\u003e8.3 Learning Architectures.\u003c\/p\u003e \u003cp\u003e8.4 Active Learning Methods.\u003c\/p\u003e \u003cp\u003e8.5 Combining Active Learning with Semi-supervised Learning.\u003c\/p\u003e \u003cp\u003e8.6 Applications.\u003c\/p\u003e \u003cp\u003e8.7 Evaluation of Active Learning Methods.\u003c\/p\u003e \u003cp\u003e8.8 Discussion and Conclusions.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART 2 SPOKEN LANGUAGE UNDERSTANDING FOR HUMAN\/HUMAN CONVERSATIONS.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Human\/Human Conversation Understanding\u003c\/b\u003e (\u003ci\u003eGokhan Tur and Dilek Hakkani-Tür\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e9.1 Background.\u003c\/p\u003e \u003cp\u003e9.2 Human\/Human Conversation Understanding Tasks.\u003c\/p\u003e \u003cp\u003e9.3 Dialogue Act Segmentation and Tagging.\u003c\/p\u003e \u003cp\u003e9.4 Action Item and Decision Detection.\u003c\/p\u003e \u003cp\u003e9.5 Addressee Detection and Co-reference Resolution.\u003c\/p\u003e \u003cp\u003e9.6 Hot Spot Detection.\u003c\/p\u003e \u003cp\u003e9.7 Subjectivity, Sentiment, and Opinion Detection.\u003c\/p\u003e \u003cp\u003e9.8 Speaker Role Detection.\u003c\/p\u003e \u003cp\u003e9.9 Modeling Dominance.\u003c\/p\u003e \u003cp\u003e9.10 Argument Diagramming.\u003c\/p\u003e \u003cp\u003e9.11 Discussion and Conclusions.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Named Entity Recognition\u003c\/b\u003e (\u003ci\u003eFrédéric Béchet\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e10.1 Task Description.\u003c\/p\u003e \u003cp\u003e10.2 Challenges Using Speech Input.\u003c\/p\u003e \u003cp\u003e10.3 Benchmark Data Sets, Applications.\u003c\/p\u003e \u003cp\u003e10.4 Evaluation Metrics.\u003c\/p\u003e \u003cp\u003e10.5 Main Approaches for Extracting NEs from Text.\u003c\/p\u003e \u003cp\u003e10.6 Comparative Methods for NER from Speech.\u003c\/p\u003e \u003cp\u003e10.7 New Trends in NER from Speech.\u003c\/p\u003e \u003cp\u003e10.8 Conclusions.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Topic Segmentation\u003c\/b\u003e (\u003ci\u003eMatthew Purver\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e11.1 Task Description.\u003c\/p\u003e \u003cp\u003e11.2 Basic Approaches, and the Challenge of Speech.\u003c\/p\u003e \u003cp\u003e11.3 Applications and Benchmark Datasets.\u003c\/p\u003e \u003cp\u003e11.4 Evaluation Metrics.\u003c\/p\u003e \u003cp\u003e11.5 Technical Approaches.\u003c\/p\u003e \u003cp\u003e11.6 New Trends and Future Directions.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Topic Identification\u003c\/b\u003e (\u003ci\u003eTimothy J. Hazen\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e12.1 Task Description.\u003c\/p\u003e \u003cp\u003e12.2 Challenges Using Speech Input.\u003c\/p\u003e \u003cp\u003e12.3 Applications and Benchmark Tasks.\u003c\/p\u003e \u003cp\u003e12.4 Evaluation Metrics.\u003c\/p\u003e \u003cp\u003e12.5 Technical Approaches.\u003c\/p\u003e \u003cp\u003e12.6 New Trends and Future Directions.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Speech Summarization\u003c\/b\u003e (\u003ci\u003eYang Liu and Dilek Hakkani-Tür\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e13.1 Task Description.\u003c\/p\u003e \u003cp\u003e13.2 Challenges when Using Speech Input.\u003c\/p\u003e \u003cp\u003e13.3 Data Sets.\u003c\/p\u003e \u003cp\u003e13.4 Evaluation Metrics.\u003c\/p\u003e \u003cp\u003e13.5 General Approaches.\u003c\/p\u003e \u003cp\u003e13.6 More Discussions on Speech versus Text Summarization.\u003c\/p\u003e \u003cp\u003e13.7 Conclusions.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Speech Analytics\u003c\/b\u003e (\u003ci\u003eI. Dan Melamed and Mazin Gilbert\u003c\/i\u003e)\u003c\/p\u003e \u003cp\u003e14.1 Introduction.\u003c\/p\u003e \u003cp\u003e14.2 System Architecture.\u003c\/p\u003e \u003cp\u003e14.3 Speech Transcription.\u003c\/p\u003e \u003cp\u003e14.4 Text Feature Extraction.\u003c\/p\u003e \u003cp\u003e14.5 Acoustic Feature Extraction.\u003c\/p\u003e \u003cp\u003e14.6 Relational Feature Extraction.\u003c\/p\u003e \u003cp\u003e14.7 DBMS.\u003c\/p\u003e \u003cp\u003e14.8 Media Server and Player.\u003c\/p\u003e \u003cp\u003e14.9 Trend Analysis.\u003c\/p\u003e \u003cp\u003e14.10 Alerting System.\u003c\/p\u003e \u003cp\u003e14.11 Conclusion.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Speech Retrieval\u003c\/b\u003e (\u003ci\u003eCiprian Chelba, Timothy J. Hazen, Bhuvana Ramabhadran and Murat Saraçlar\u003c\/i\u003e).\u003c\/p\u003e \u003cp\u003e15.1 Task Description.\u003c\/p\u003e \u003cp\u003e15.2 Applications.\u003c\/p\u003e \u003cp\u003e15.3 Challenges Using Speech Input.\u003c\/p\u003e \u003cp\u003e15.4 Evaluation Metrics.\u003c\/p\u003e \u003cp\u003e15.5 Benchmark Data Sets.\u003c\/p\u003e \u003cp\u003e15.6 Approaches.\u003c\/p\u003e \u003cp\u003e15.7 New Trends.\u003c\/p\u003e \u003cp\u003e15.8 Discussion and Conclusions.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIndex.\u003c\/b\u003e\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Electronics \u0026amp; communications engineering [\u003ca title=\"See our other books on Electronics \u0026amp; communications engineering\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Electronics%20\u0026amp;%20communications%20engineering%20%5BTJ%5D%22\"\u003eTJ\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley","offers":[{"title":"Brand New","offer_id":52278021259544,"sku":"9780470688243","price":85.35,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9780470688243.jpg?v=1781455808","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/spoken-language-understanding-systems-for-extracting-semantic-information-from-speech-hardback-9780470688243","provider":"Freshly Printed Books","version":"1.0","type":"link"}