Pattern Recognition In Speech And Language Processing
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Pattern Recognition in Speech and Language Processing
Over the last 20 years, approaches to designing speech and language processing algorithms have moved from methods based on linguistics and speech science to data-driven pattern recognition techniques. These techniques have been the focus of intense, fast-moving research and have contributed to significant advances in this field. Pattern Recognition in Speech and Language Processing offers a systematic, up-to-date presentation of these recent developments. It begins with the fundamentals and recent theoretical advances in pattern recognition, with emphasis on classifier design criteria and optimization procedures. The focus then shifts to the application of these techniques to speech processing, with chapters exploring advances in applying pattern recognition to real speech and audio processing systems. The final section of the book examines topics related to pattern recognition in language processing: topics that represent promising new trends with direct impact on information processing systems for the Web, broadcast news, and other content-rich information resources. Each self-contained chapter includes figures, tables, diagrams, and references. The collective effort of experts at the forefront of the field, Pattern Recognition in Speech and Language Processing offers in-depth, insightful discussions on new developments and contains a wealth of information integral to the further development of human-machine communications.
Speech and Language Processing
This book takes an empirical approach to language processing, based on applying statistical and other machine-learning algorithms to large corpora.Methodology boxes are included in each chapter. Each chapter is built around one or more worked examples to demonstrate the main idea of the chapter. Covers the fundamental algorithms of various fields, whether originally proposed for spoken or written language to demonstrate how the same algorithm can be used for speech recognition and word-sense disambiguation. Emphasis on web and other practical applications. Emphasis on scientific evaluation. Useful as a reference for professionals in any of the areas of speech and language processing.
Machine Learning and Data Mining in Pattern Recognition
Author: Petra Perner
language: en
Publisher: Springer Science & Business Media
Release Date: 2009-07-21
There is no royal road to science, and only those who do not dread the fatiguing climb of its steep paths have a chance of gaining its luminous summits. Karl Marx A Universial Genius of the 19th Century Many scientists from all over the world during the past two years since the MLDM 2007 have come along on the stony way to the sunny summit of science and have worked hard on new ideas and applications in the area of data mining in pattern r- ognition. Our thanks go to all those who took part in this year's MLDM. We appre- ate their submissions and the ideas shared with the Program Committee. We received over 205 submissions from all over the world to the International Conference on - chine Learning and Data Mining, MLDM 2009. The Program Committee carefully selected the best papers for this year’s program and gave detailed comments on each submitted paper. There were 63 papers selected for oral presentation and 17 papers for poster presentation. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data-mining methods for the different multimedia data types such as image mining, text mining, video mining and Web mining. Among these topics this year were special contributions to subtopics such as attribute discre- zation and data preparation, novelty and outlier detection, and distances and simila- ties.