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Automatic determination of acoustic model topology using variational Bayesian estimation and clustering for large vocabulary continuous speech recognition

机译:基于变分贝叶斯估计和聚类的大词汇量连续语音识别自动确定声学模型拓扑

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摘要

We describe the automatic determination of a large and complicated acoustic model for speech recognition by using variational Bayesian estimation and clustering (VBEC) for speech recognition. We propose an efficient method for decision tree clustering based on a Gaussian mixture model (GMM) and an efficient model search algorithm for finding an appropriate acoustic model topology within the VBEC framework. GMM-based decision tree clustering for triphone HMM states features a novel approach designed to reduce the overly large number of computations to a practical level by utilizing the statistics of monophone hidden Markov model states. The model search algorithm also reduces the search space by utilizing the characteristics of the acoustic model. The experimental results confirmed that VBEC automatically and rapidly yielded an optimum model topology with the highest performance.
机译:我们描述了通过使用变分贝叶斯估计和聚类(VBEC)进行语音识别的大型复杂语音模型的自动确定。我们提出了一种基于高斯混合模型(GMM)的决策树聚类的有效方法,以及一种用于在VBEC框架内找到合适的声学模型拓扑的有效模型搜索算法。用于三音器HMM状态的基于GMM的决策树聚类具有一种新颖的方法,该方法旨在通过利用单音器隐马尔可夫模型状态的统计信息将过多的计算量减少到实际水平。模型搜索算法还通过利用声学模型的特性来减少搜索空间。实验结果证实,VBEC可以自动快速生成具有最高性能的最佳模型拓扑。

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