Download Automatic Speech and Speaker Recognition: Large Margin and by Joseph Keshet, Samy Bengio PDF

By Joseph Keshet, Samy Bengio

This publication discusses huge margin and kernel equipment for speech and speaker popularity

Speech and Speaker reputation: huge Margin and Kernel equipment is a collation of study within the contemporary advances in huge margin and kernel equipment, as utilized to the sphere of speech and speaker attractiveness. It provides theoretical and functional foundations of those tools, from aid vector machines to massive margin equipment for dependent studying. It additionally presents examples of enormous margin dependent acoustic modelling for non-stop speech recognizers, the place the grounds for sensible huge margin series studying are set. huge margin equipment for discriminative language modelling and textual content self sustaining speaker verification also are addressed during this e-book.

Key positive aspects:

  • Provides an updated image of the present country of analysis during this box
  • Covers vital features of extending the binary help vector laptop to speech and speaker attractiveness functions
  • Discusses huge margin and kernel process algorithms for series prediction required for acoustic modeling
  • Reviews earlier and current paintings on discriminative education of language types, and describes various huge margin algorithms for the applying of part-of-speech tagging
  • Surveys fresh paintings at the use of kernel methods to text-independent speaker verification, and introduces the most thoughts and algorithms
  • Surveys fresh paintings on kernel techniques to studying a similarity matrix from facts

This booklet may be of curiosity to researchers, practitioners, engineers, and scientists in speech processing and desktop studying fields.

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Extra resources for Automatic Speech and Speaker Recognition: Large Margin and Kernel Methods

Example text

The focus of traditional optimization approaches is obtaining fast convergence to the optimum f (w∗ ) = minw f (w). Typically linear, or even quadratic, convergence is guaranteed. That is, a solution with ˜ ≤ f (w∗ ) + is obtained after O(log(1/ )) or even O(log log(1/ )) iterations. These f (w) methods are therefore especially well suited in situations in which very accurate solutions are necessary. As mentioned above, this might come at the expense of a very sharp runtime dependence on the number of training examples or other problem parameters.

The target semi-order constitutes a set of six relation pairs, induced from a multi-class multi-label problem which ranks categories 1, 2 above categories 3, 4, 5. The prediction total-order is given by yˆ = {(1, 3), (3, 4), (4, 2), (2, 5)}. The four covers χ i for i = 1, 2, 3, 4 correspond to the four covers defined in the text, the index number of the title corresponds to the specific elements for the cover. Each plot corresponds to a specific element in a specific cover. A solid edge designates an order-pair in which the total-ordering yˆ is consistent, and a dotted edge designates a pair in which the total-order is not consistent with the semi-order.

In other words, we would like to know whether an error occurred or not. However, with respect to the problem of document categorization, where each document is associated with a subset of relevant categories, it seems more natural to ask how many categories are mismatched by the total-order. To highlight this observation, we slightly modify our definitions and denote an example by a triplet (x, y, χ ): an instance x, a target semi-order y and a cover χ of y. Thus the choice of loss is part of the problem description and is not a task of the learning algorithm.

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