The subspace Gaussian mixture model-A structured model for speech recognition
The subspace Gaussian mixture model-A structured model for speech recognition
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DOI：
10.1016/j.csl.2010.06.003
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发表时间：
2011-04-01
影响因子：
4.3
通讯作者：
Thomas, Samuel
中科院分区：
文献类型：
--
作者：
Povey, Daniel;Burget, Lukas;Thomas, Samuel
我们描述了一种新的语音识别方法，其中所有的隐马尔可夫模型(HMM)状态共享相同的高斯混合模型(GMM)结构，每个状态中具有相同数量的高斯样本。该模型由与具有例如50维的每个状态相关联的向量以及从该向量空间到GMM的参数空间的全局映射来定义。这种模型似乎比传统模型提供了更好的结果，额外的结构为建模创新提供了许多新的机会，同时保持了与大多数标准技术的兼容性。(C)2010爱思唯尔有限公司。保留所有权利。
We describe a new approach to speech recognition, in which all Hidden Markov Model (HMM) states share the same Gaussian Mixture Model (GMM) structure with the same number of Gaussians in each state. The model is defined by vectors associated with each state with a dimension of, say, 50, together with a global mapping from this vector space to the space of parameters of the GMM. This model appears to give better results than a conventional model, and the extra structure offers many new opportunities for modeling innovations while maintaining compatibility with most standard techniques. (C) 2010 Elsevier Ltd. All rights reserved.