鉴于自动语音识别(ASR)中置信度估计困难的问题,该文提出一种基于多知识源融合的策略来提高置信度的鉴别能力。具体做法是,首先选择关于识别结果的声学层、语言层和语义层等不同层次的信息,然后通过实验确定这些信息不同的组合方式,并...鉴于自动语音识别(ASR)中置信度估计困难的问题,该文提出一种基于多知识源融合的策略来提高置信度的鉴别能力。具体做法是,首先选择关于识别结果的声学层、语言层和语义层等不同层次的信息,然后通过实验确定这些信息不同的组合方式,并以此为特征在隐藏单元条件随机场(Hidden-units Conditional Random Fields,HuCRFs)框架下计算识别结果的条件概率。最后将HuCRFs条件概率作为语音识别结果置信度的新的估计。实验首先证明了HuCRFs条件概率是比归一化的网格后验概率鉴别能力更强的一种置信度估计方法。同时基于HuCRFs条件概率置信度,对解码器一遍识别得到的网格重新搜索最佳候选序列,取得了相对一遍识别最佳候选序列绝对近2%的字错误率(CER)下降。同时,该文也对比了基于HuCRFs条件概率搜索的最佳候选序列和基于长语言模型网格重估的最佳候选序列的性能,进一步证明了使用HuCRFs条件概率作为置信度估计是一种更好的选择。展开更多
Based on the fact that 3-D model discretization by artificial could not always be successfully implemented especially for large-scaled problems when high accuracy and efficiency were required, a new adaptive multigrid...Based on the fact that 3-D model discretization by artificial could not always be successfully implemented especially for large-scaled problems when high accuracy and efficiency were required, a new adaptive multigrid finite element method was proposed. In this algorithm, a-posteriori error estimator was employed to generate adaptively refined mesh on a given initial mesh. On these iterative meshes, V-cycle based multigrid method was adopted to fast solve each linear equation with each initial iterative term interpolated from last mesh. With this error estimator, the unknowns were nearly optimally distributed on the final mesh which guaranteed the accuracy. The numerical results show that the multigrid solver is faster and more stable compared with ICCG solver. Meanwhile, the numerical results obtained from the final model discretization approximate the analytical solutions with maximal relative errors less than 1%, which remarkably validates this algorithm.展开更多
文摘鉴于自动语音识别(ASR)中置信度估计困难的问题,该文提出一种基于多知识源融合的策略来提高置信度的鉴别能力。具体做法是,首先选择关于识别结果的声学层、语言层和语义层等不同层次的信息,然后通过实验确定这些信息不同的组合方式,并以此为特征在隐藏单元条件随机场(Hidden-units Conditional Random Fields,HuCRFs)框架下计算识别结果的条件概率。最后将HuCRFs条件概率作为语音识别结果置信度的新的估计。实验首先证明了HuCRFs条件概率是比归一化的网格后验概率鉴别能力更强的一种置信度估计方法。同时基于HuCRFs条件概率置信度,对解码器一遍识别得到的网格重新搜索最佳候选序列,取得了相对一遍识别最佳候选序列绝对近2%的字错误率(CER)下降。同时,该文也对比了基于HuCRFs条件概率搜索的最佳候选序列和基于长语言模型网格重估的最佳候选序列的性能,进一步证明了使用HuCRFs条件概率作为置信度估计是一种更好的选择。
基金Projects(2006AA06Z105, 2007AA06Z134) supported by the National High-Tech Research and Development Program of ChinaProjects(2007, 2008) supported by China Scholarship Council (CSC)
文摘Based on the fact that 3-D model discretization by artificial could not always be successfully implemented especially for large-scaled problems when high accuracy and efficiency were required, a new adaptive multigrid finite element method was proposed. In this algorithm, a-posteriori error estimator was employed to generate adaptively refined mesh on a given initial mesh. On these iterative meshes, V-cycle based multigrid method was adopted to fast solve each linear equation with each initial iterative term interpolated from last mesh. With this error estimator, the unknowns were nearly optimally distributed on the final mesh which guaranteed the accuracy. The numerical results show that the multigrid solver is faster and more stable compared with ICCG solver. Meanwhile, the numerical results obtained from the final model discretization approximate the analytical solutions with maximal relative errors less than 1%, which remarkably validates this algorithm.
