1000 resultados para Jaddua (Biblical figure)


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La création cinématographique de l’étudiant qui accompagne ce mémoire sous la forme d’un DVD est disponible à la Médiathèque de la Bibliothèque des lettres et sciences humaines sous le titre : She is Lars.(http://atrium.umontreal.ca/notice/UM-ALEPH002343877)

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Dedicated to John A. Heraud, then editor of the Monthly magazine, which published a portion of this translation in the issue for October, 1839.

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Mode of access: Internet.

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Mode of access: Internet.

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Includes Hebrew text of Num. XXII-XXIV and book of Jonah.

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Mode of access: Internet.

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Includes bibliographical references.

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This paper introduces a novel technique to directly optimise the Figure of Merit (FOM) for phonetic spoken term detection. The FOM is a popular measure of sTD accuracy, making it an ideal candiate for use as an objective function. A simple linear model is introduced to transform the phone log-posterior probabilities output by a phe classifier to produce enhanced log-posterior features that are more suitable for the STD task. Direct optimisation of the FOM is then performed by training the parameters of this model using a non-linear gradient descent algorithm. Substantial FOM improvements of 11% relative are achieved on held-out evaluation data, demonstrating the generalisability of the approach.

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This work proposes to improve spoken term detection (STD) accuracy by optimising the Figure of Merit (FOM). In this article, the index takes the form of phonetic posterior-feature matrix. Accuracy is improved by formulating STD as a discriminative training problem and directly optimising the FOM, through its use as an objective function to train a transformation of the index. The outcome of indexing is then a matrix of enhanced posterior-features that are directly tailored for the STD task. The technique is shown to improve the FOM by up to 13% on held-out data. Additional analysis explores the effect of the technique on phone recognition accuracy, examines the actual values of the learned transform, and demonstrates that using an extended training data set results in further improvement in the FOM.