The Experts below are selected from a list of 37932 Experts worldwide ranked by ideXlab platform
Ayumi Shinohara - One of the best experts on this subject based on the ideXlab platform.
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String Pattern Discovery
Lecture Notes in Computer Science, 2004Co-Authors: Ayumi ShinoharaAbstract:Finding a good Pattern which discriminates one set of strings from the other set is a critical task in knowledge Discovery. In this paper, we review a series of our works concerning with the string Pattern Discovery. It includes theoretical analyses of learnabilities of some Pattern classes, as well as development of practical data structures which support efficient string processing.
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ALT - String Pattern Discovery
Lecture Notes in Computer Science, 2004Co-Authors: Ayumi ShinoharaAbstract:Finding a good Pattern which discriminates one set of strings from the other set is a critical task in knowledge Discovery. In this paper, we review a series of our works concerning with the string Pattern Discovery. It includes theoretical analyses of learnabilities of some Pattern classes, as well as development of practical data structures which support efficient string processing.
James Glass - One of the best experts on this subject based on the ideXlab platform.
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personalized mispronunciation detection and diagnosis based on unsupervised error Pattern Discovery
International Conference on Acoustics Speech and Signal Processing, 2016Co-Authors: Nancy F Chen, James GlassAbstract:In this work, we introduce two improvements to our previously proposed mispronunciation detection framework. The framework focuses on each learner individually and consists of two main procedures: unsupervised error Pattern Discovery and pronunciation error decoding. First, we propose nbest filtering to disambiguate uncertain error candidate hypotheses obtained from acoustic similarity clustering. Second, we propose personalized template-based rescoring to refine the mispronunciation detection results. The second contribution of the paper is that we demonstrate the portability of the framework to a new target language. Experimental results on the iCALL corpus, a nonnative Mandarin corpus consisting of speakers of European origin, show that the new error Pattern Discovery process significantly reduces the size and increases the coverage of the error candidate set. Also, the rescoring technique effectively improves system performance on mispronunciation detection and diagnosis.
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Unsupervised Pattern Discovery in Speech
IEEE Transactions on Audio Speech and Language Processing, 2008Co-Authors: A.s. Park, James GlassAbstract:We present a novel approach to speech processing based on the principle of Pattern Discovery. Our work represents a departure from traditional models of speech recognition, where the end goal is to classify speech into categories defined by a prespecified inventory of lexical units (i.e., phones or words). Instead, we attempt to discover such an inventory in an unsupervised manner by exploiting the structure of repeating Patterns within the speech signal. We show how Pattern Discovery can be used to automatically acquire lexical entities directly from an untranscribed audio stream. Our approach to unsupervised word acquisition utilizes a segmental variant of a widely used dynamic programming technique, which allows us to find matching acoustic Patterns between spoken utterances. By aggregating information about these matching Patterns across audio streams, we demonstrate how to group similar acoustic sequences together to form clusters corresponding to lexical entities such as words and short multiword phrases. On a corpus of academic lecture material, we demonstrate that clusters found using this technique exhibit high purity and that many of the corresponding lexical identities are relevant to the underlying audio stream.
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Unsupervised Pattern Discovery in speech: applications to word acquisition and speaker segmentation
2007Co-Authors: James Glass, Alex ParkAbstract:We present a novel approach to speech processing based on the principle of Pattern Discovery. Our work represents a departure from traditional models of speech recognition, where the end goal is to classify speech into categories defined by a pre-specified inventory of lexical units (i.e. phones or words). Instead, we attempt to discover such an inventory in an unsupervised manner by exploiting the structure of repeating Patterns within the speech signal. We show how Pattern Discovery can be used to automatically acquire lexical entities directly from an untranscribed audio stream. Our approach to unsupervised word acquisition utilizes a segmental variant of a widely used dynamic programming technique, which allows us to find matching acoustic Patterns between spoken utterances. By aggregating information about these matching Patterns across audio streams, we demonstrate how to group similar acoustic sequences together to form clusters corresponding to lexical entities such as words and short multi-word phrases. On a corpus of academic lecture material, we demonstrate that clusters found using this technique exhibit high purity and that many of the corresponding lexical identities are relevant to the underlying audio stream. We demonstrate two applications of our Pattern Discovery procedure. First, we propose and evaluate two methods for automatically identifying sound clusters generated through Pattern Discovery. Our results show that high identification accuracy can be achieved for single word clusters using a constrained isolated word recognizer. Second, we apply acoustic Pattern matching to the problem of speaker segmentation by attempting to find word-level speech Patterns that are repeated by the same speaker. When used to segment a ten hour corpus of multi-speaker lectures, we found that our approach is able to generate segmentations that correlate well to independently generated human segmentations. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)
Yang Ouyang - One of the best experts on this subject based on the ideXlab platform.
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ICEBE - Service Pattern Discovery of Web Service Mining in Web Service Registry-Repository
2006 IEEE International Conference on e-Business Engineering (ICEBE'06), 2006Co-Authors: Qianhui Althea Liang, Jen-yao Chung, Steven M. Miller, Yang OuyangAbstract:This paper presents and elaborates the concept of Web service usage Patterns and Pattern Discovery through service mining. We define three different levels of service usage data: i) user request level, ii) template level and iii) instance level. At each level, we investigate Patterns of service usage data and the Discovery of these Patterns. An algorithm for service Pattern Discovery at the template level is presented. We show the system architecture of a service-mining enabled service registry repository. Web service Patterns, Pattern Discovery and Pattern mining supports the Discovery and composition of complex services, which in turn supports the application of web services to increasingly complex business processes and applications.
Geoffrey I. Webb - One of the best experts on this subject based on the ideXlab platform.
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A tutorial on statistically sound Pattern Discovery
Data Mining and Knowledge Discovery, 2019Co-Authors: Wilhelmiina Hämäläinen, Geoffrey I. WebbAbstract:Statistically sound Pattern Discovery harnesses the rigour of statistical hypothesis testing to overcome many of the issues that have hampered standard data mining approaches to Pattern Discovery. Most importantly, application of appropriate statistical tests allows precise control over the risk of false discoveries—Patterns that are found in the sample data but do not hold in the wider population from which the sample was drawn. Statistical tests can also be applied to filter out Patterns that are unlikely to be useful, removing uninformative variations of the key Patterns in the data. This tutorial introduces the key statistical and data mining theory and techniques that underpin this fast developing field. We concentrate on two general classes of Patterns: dependency rules that express statistical dependencies between condition and consequent parts and dependency sets that express mutual dependence between set elements. We clarify alternative interpretations of statistical dependence and introduce appropriate tests for evaluating statistical significance of Patterns in different situations. We also introduce special techniques for controlling the likelihood of spurious discoveries when multitudes of Patterns are evaluated. The paper is aimed at a wide variety of audiences. It provides the necessary statistical background and summary of the state-of-the-art for any data mining researcher or practitioner wishing to enter or understand statistically sound Pattern Discovery research or practice. It can serve as a general introduction to the field of statistically sound Pattern Discovery for any reader with a general background in data sciences.
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KDD - Statistically sound Pattern Discovery
Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '14, 2014Co-Authors: Wilhelmiina Hämäläinen, Geoffrey I. WebbAbstract:Pattern Discovery is a core data mining activity. Initial approaches were dominated by the frequent Pattern Discovery paradigm -- only Patterns that occur frequently in the data were explored. Having been thoroughly researched and its limitations now well understood, this paradigm is giving way to a new one, which can be called statistically sound Pattern Discovery. In this paradigm, the main impetus is to discover statistically significant Patterns, which are unlikely to have occurred by chance and are likely to hold in future data. Thus, the new paradigm provides a strict control over false discoveries and overfitting. This tutorial covers both classic and cutting-edge research topics on Pattern Discovery combined to statistical significance testing. We start with an advanced introduction to the relevant forms of statistical significance testing, including different schools and alternative models, their underlying assumptions, practical issues, and limitations. We then discuss their application to data mining specific problems, including evaluation of nested Patterns, the multiple testing problem, algorithmic strategies and real-world considerations. We present the current state-of-the art solutions and explore in detail how this approach to Pattern Discovery can deliver efficient and effective Discovery of small sets of interesting Patterns.
Qianhui Althea Liang - One of the best experts on this subject based on the ideXlab platform.
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ICEBE - Service Pattern Discovery of Web Service Mining in Web Service Registry-Repository
2006 IEEE International Conference on e-Business Engineering (ICEBE'06), 2006Co-Authors: Qianhui Althea Liang, Jen-yao Chung, Steven M. Miller, Yang OuyangAbstract:This paper presents and elaborates the concept of Web service usage Patterns and Pattern Discovery through service mining. We define three different levels of service usage data: i) user request level, ii) template level and iii) instance level. At each level, we investigate Patterns of service usage data and the Discovery of these Patterns. An algorithm for service Pattern Discovery at the template level is presented. We show the system architecture of a service-mining enabled service registry repository. Web service Patterns, Pattern Discovery and Pattern mining supports the Discovery and composition of complex services, which in turn supports the application of web services to increasingly complex business processes and applications.