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高斯相似度分析
相关语句
  gaussian similarity analysis
     Interpolation Adaptation Algorithm Based on Gaussian Similarity Analysis
     基于高斯相似度分析的插值自适应算法
短句来源
  相似匹配句对
     Gaussian and Non-Gaussian Self-Similarity of Turbulence
     湍流的高斯和非高斯相似
     RECURSIVE SIMILARITY
     递归相似
短句来源
     The hemagglutination spectrum was similar with ND vaccine of Muktesw ar, HB1 and LaSota strains.
     和 Lasota相似
短句来源
     ON U-SIMILAR OF HERMITE U-MATRIX IN GAUSS RINGS Z[i]
     高斯数环Z[i]上Hermite-酉矩阵的酉相似
短句来源
     Interpolation Adaptation Algorithm Based on Gaussian Similarity Analysis
     基于高斯相似分析的插值自适应算法
短句来源
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The performance of speech recognition system will be significantly deteriorated because of the mismatches between training and testing conditions. This paper addresses the problem and proposes an environment adaptation algorithm to adapt the mean vectors of HMM. The algorithm can reduce the performance deterioration of the speech recognition system caused by the mismatches. Firstly, we build a binary tree by Gaussian similarity analysis (GSA) and then adaptively adjust the class number according to the data....

The performance of speech recognition system will be significantly deteriorated because of the mismatches between training and testing conditions. This paper addresses the problem and proposes an environment adaptation algorithm to adapt the mean vectors of HMM. The algorithm can reduce the performance deterioration of the speech recognition system caused by the mismatches. Firstly, we build a binary tree by Gaussian similarity analysis (GSA) and then adaptively adjust the class number according to the data. In each class, we adapt the HMM using nonlinear transform approximated by piecewise linear regression. Rather than using maximum likelihood estimation (MLE) in estimating the transformation parameters, we propose using maximum a posteriori (MAP) as the estimation criterion. The proposed algorithm, called GAS-MAPNT, has been evaluated on a Chinese digit recognition experiment based on continuous density HMM. The test shows that the proposed algorithm is efficient and superior to other algorithms with Gaussian similarity analysis, such as maximum a posteriori linear regression (MAPLR) algorithm and maximum likelihood linear regression (MLLR) algorithm.

由于训练环境和识别环境的失配,识别系统的性能会严重下降,为此提出了基于高斯相似度分析的最大后验概率非线性变换的环境自适应算法,它可以减小由于环境的失配所引起的系统性能的下降.在该算法中,首先将HMM模型中的高斯分量进行相似度分析并建立二叉树,然后根据数据自适应调整变换类数,在每一类内利用分段线性回归近似非线性变换将训练环境下的HMM变换到识别环境,减小环境的失配,变换参数的估计采用了最大后验概率估计(MAP).数字语音识别实验证明:该环境自适应算法的识别性能优于带有高斯相似度分析的MLST、MAPLR和MLLR等算法.

 
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