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Mixtures of inverse covariances: Covariance modeling for Gaussian mixtures with applications to automatic speech recognition.

机译：逆协方差的混合：高斯混合的协方差建模及其在自动语音识别中的应用。

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摘要

Gaussian mixture models (GMM) are widely used in statistical pattern recognition for a variety of tasks ranging from image classification to automatic speech recognition. Because of the large number of parameters devoted to representing Gaussian covariances in these models, their scalability to problems involving a large number of dimensions and a large number of Gaussian components is limited. In particular, this shortcoming of Gaussian mixture models affects the accuracy of real-time speech recognition systems by limiting the complexity of the mixtures used for acoustic modeling.; This thesis addresses the scalability problems of Gaussian mixtures through a class of models, collectively called “mixtures of inverse covariances” or MIC, which approximate the inverse covariances in a Gaussian mixture while significantly reducing both the number of parameters to be estimated, and the computations required to evaluate the Gaussian likelihoods. The MIC model scales well to problems involving large number of Gaussians and large dimensionalities, opening up new possibilities in the design of efficient and accurate statistical models. In particular, when applying these models to acoustic modeling for real-world automatic speech recognition tasks, they significantly improve both the speed and accuracy of a state-of-the-art speech recognition system.
机译：高斯混合模型（GMM）被广泛用于统计模式识别，用于从图像分类到自动语音识别的各种任务。由于在这些模型中有大量参数用于表示高斯协方差，因此它们在涉及大量维数和大量高斯分量的问题上的可扩展性受到限制。特别是，高斯混合模型的这一缺陷通过限制用于声学建模的混合模型的复杂性而影响了实时语音识别系统的准确性。本文通过一类模型来解决高斯混合的可伸缩性问题，这些模型统称为“逆协方差的混合”或MIC，该模型近似估计高斯混合中的逆协方差，同时显着减少了要估计的参数数量和计算量。需要评估高斯似然。 MIC模型可以很好地解决涉及大量高斯和大维度的问题，从而为设计高效，准确的统计模型开辟了新的可能性。特别是，将这些模型应用于真实世界中自动语音识别任务的声学模型时，它们可以显着提高最新语音识别系统的速度和准确性。

著录项

  • 作者

    Vanhoucke, Vincent.;

  • 作者单位

    Stanford University.;

  • 授予单位 Stanford University.;
  • 学科 Engineering Electronics and Electrical.; Computer Science.
  • 学位 Ph.D.
  • 年度 2004
  • 页码 p.5696
  • 总页数 147
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 无线电电子学、电信技术;
  • 关键词

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