kaldi increasing GMM components in the training procedure

本文介绍如何使用连续高斯分布建立初始未绑定模型集,在训练数据稀疏的情况下,简化单高斯分布的应用。随着绑定过程进行,每个状态获得足够数据后,可以进一步估计复杂的混合高斯分布,从而提高准确性。

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This (continuous Gaussian distribution) allows simple single Gaussian distributions to be used for an initial untied model set where the training data is very patchy. Then once tying has been performed such that every state has an adequate amount of data, more complex mixture Gaussian distributions can be estimated to give increased accuracy.

Reference

  • Young, Steve J., Julian J. Odell, and Philip C. Woodland. “Tree-based state tying for high accuracy acoustic modelling.” Proceedings of the workshop on Human Language Technology. Association for Computational Linguistics, 1994.
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