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This book constitutes the thoroughly refereed proceedings of the 5th International Symposium on Chinese Spoken Language Processing, ISCSLP 2006, held in Singapore in December 2006, co-located with ICCPOL 2006, the 21st International Conference on Computer Processing of Oriental Languages. Coverage includes speech science, acoustic modeling for automatic speech recognition, speech data mining, and machine translation of speech.
After decades of research activity, Chinese spoken language processing (CSLP) has advanced considerably both in practical technology and theoretical discovery. In this book, the editors provide both an introduction to the field as well as unique research problems with their solutions in various areas of CSLP. The contributions represent pioneering efforts ranging from CSLP principles to technologies and applications, with each chapter encapsulating a single problem and its solutions.A commemorative volume for the 10th anniversary of the international symposium on CSLP in Singapore, this is a valuable reference for established researchers and an excellent introduction for those interested in the area of CSLP.
This book presents advances in speech and music in the domain of audio signal processing. The book begins with introductory chapters on the basics of speech and music, and then proceeds to computational aspects of speech and music, including music information retrieval and spoken language processing. The authors discuss the intersection in the field of computer science, musicology and speech analysis, and how the multifaceted nature of speech and music information processing requires unique algorithms, systems using sophisticated signal processing, and machine learning techniques that better extract useful information. The authors discuss how a deep understanding of both speech and music in terms of perception, emotion, mood, gesture and cognition is essential for successful application. Also discussed is the overwhelming amount of data that has been generated across the world that requires efficient processing for better maintenance, retrieval, indexing and querying and how machine learning and artificial intelligence are most suited for these computational tasks. The book provides both technological knowledge and a comprehensive treatment of essential topics in speech and music processing.
This book constitutes the proceedings of the 24th International Conference on Text, Speech, and Dialogue, TSD 2021, held in Olomouc, Czech Republic, in September 2021.* The 2 keynote speeches and 46 papers presented in this volume were carefully reviewed and selected from 101 submissions. The topical sections "Text", "Speech", and "Dialogue" deal with the following issues: speech recognition; corpora and language resources; speech and spoken language generation; tagging, classification and parsing of text and speech; semantic processing of text and speech; integrating applications of text and speech processing; automatic dialogue systems; multimodal techniques and modelling, and others. * Due to the COVID-19 pandemic the conference was held in a "hybrid" mode.
Conventional computational methods, and even the latest soft computing paradigms, often fall short in their ability to offer solutions to many real-world problems due to uncertainty, imprecision, and circumstantial data. Hybrid intelligent computing is a paradigm that addresses these issues to a considerable extent. The Handbook of Research on Advanced Hybrid Intelligent Techniques and Applications highlights the latest research on various issues relating to the hybridization of artificial intelligence, practical applications, and best methods for implementation. Focusing on key interdisciplinary computational intelligence research dealing with soft computing techniques, pattern mining, data analysis, and computer vision, this book is relevant to the research needs of academics, IT specialists, and graduate-level students.
As technology spreads globally, researchers and scientists continue to develop and study the strategy behind creating artificial life. This research field is ever expanding, and it is essential to stay current in the contemporary trends in artificial life, artificial intelligence, and machine learning. This an important topic for researchers and scientists in the field as well as industry leaders who may adapt this technology. The Handbook of Research on New Investigations in Artificial Life, AI, and Machine Learning provides concepts, theories, systems, technologies, and procedures that exhibit properties, phenomena, or abilities of any living system or human. This major reference work includes the most up-to-date research on techniques and technologies supporting AI and machine learning. Covering topics such as behavior classification, quality control, and smart medical devices, it serves as an essential resource for graduate students, academicians, stakeholders, practitioners, and researchers and scientists studying artificial life, cognition, AI, biological inspiration, machine learning, and more.
This book discusses human emotion recognition from face images using different modalities, highlighting key topics in facial expression recognition, such as the grid formation, distance signature, shape signature, texture signature, feature selection, classifier design, and the combination of signatures to improve emotion recognition. The book explains how six basic human emotions can be recognized in various face images of the same person, as well as those available from benchmark face image databases like CK+, JAFFE, MMI, and MUG. The authors present the concept of signatures for different characteristics such as distance and shape texture, and describe the use of associated stability indices as features, supplementing the feature set with statistical parameters such as range, skewedness, kurtosis, and entropy. In addition, they demonstrate that experiments with such feature choices offer impressive results, and that performance can be further improved by combining the signatures rather than using them individually. There is an increasing demand for emotion recognition in diverse fields, including psychotherapy, biomedicine, and security in government, public and private agencies. This book offers a valuable resource for researchers working in these areas.
The main subject matter of the book is related to the demands of research and industrial centers for diagnostics, monitoring, and decision-making systems that result from the increasing complexity of automation and systems, the need to ensure the highest level of reliability and safety, and continuing research and the development of innovative approaches to fault diagnosis. Most welcome are combinations of domains of engineering knowledge for diagnosis, including detection, isolation, localization, identification, reconfiguration, and fault-tolerant control. This field is open to new challenges, including industrial diagnostics, diagnostics of computer systems and networks, as well as non-industrial applications in the form of medical diagnostics, especially those based on artificial intelligence and deep neural networks. Our community is mainly interested in the following six topics: fault detection, isolation, and identification (FDI); (ii) fault-tolerant control systems; (iii) process safety, quality, and reliability; (iv) medical diagnostics; as well as (v) methodologies based on mathematical modeling, parameter identification and state estimation, qualitative models, statistical and signal processing, artificial intelligence, fuzzy logic and rough sets, expert systems, neural networks; and (vi) industrial applications of diagnostics in fault-tolerant problems, safety, monitoring and alarming, quality control, computer systems and networks, diagnostic software, software reliability, medicine and therapy, environment protection, production control, and other industries such as chemistry, electronics, and power systems. The book is divided into six parts: (I) Artificial Intelligence in Medicine; (II) Cybersecurity; (III) Artificial Neural Networks; (IV) Fault Detection; (V) Systems Modeling; and (VI) Adaptive, Robust and FTC Systems.
Multilingual Natural Language Processing Applications is the first comprehensive single-source guide to building robust and accurate multilingual NLP systems. Edited by two leading experts, it integrates cutting-edge advances with practical solutions drawn from extensive field experience. Part I introduces the core concepts and theoretical foundations of modern multilingual natural language processing, presenting today’s best practices for understanding word and document structure, analyzing syntax, modeling language, recognizing entailment, and detecting redundancy. Part II thoroughly addresses the practical considerations associated with building real-world applications, including information extraction, machine translation, information retrieval/search, summarization, question answering, distillation, processing pipelines, and more. This book contains important new contributions from leading researchers at IBM, Google, Microsoft, Thomson Reuters, BBN, CMU, University of Edinburgh, University of Washington, University of North Texas, and others. Coverage includes Core NLP problems, and today’s best algorithms for attacking them Processing the diverse morphologies present in the world’s languages Uncovering syntactical structure, parsing semantics, using semantic role labeling, and scoring grammaticality Recognizing inferences, subjectivity, and opinion polarity Managing key algorithmic and design tradeoffs in real-world applications Extracting information via mention detection, coreference resolution, and events Building large-scale systems for machine translation, information retrieval, and summarization Answering complex questions through distillation and other advanced techniques Creating dialog systems that leverage advances in speech recognition, synthesis, and dialog management Constructing common infrastructure for multiple multilingual text processing applications This book will be invaluable for all engineers, software developers, researchers, and graduate students who want to process large quantities of text in multiple languages, in any environment: government, corporate, or academic.