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噪声环境下基于最大后验非线性变换的隐马尔可夫模型自适应算法

Hidden Markov model adaptation algorithm using maximum a posteriori nonlinear transformation in noisy environments

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【作者】 刘海滨吴镇扬赵力曾毓敏

【Author】 LIU Haibin WU Zhenyang ZHAO Li ZENG Yumin(Dept. of Radio Engineering, Southeast University Nanjing 210096) (Dept. of Physics, Nanjing Normal University Nanjing 210097)

【机构】 东南大学无线电系南京师范大学物理系 南京 210096南京 210096南京 210097

【摘要】 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,提出了基于最大后验概率非线性变换的环境自适应算法,可以减小由于环境的失配所引起的系统性能的下降。在本算法中,利用分段线性回归近似非线性变换将训练环境下隐马尔可夫模型(HMM)的均值向量变换到识别环境,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MLST,MAPLR和MLLR等算法。

【Abstract】 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 using nonlinear transformation, which is approximated by piecewise linear regression. The algorithm can reduce the performance deterioration of the speech recognition system caused by the mismatches. Rather than estimating the transformation parameters using maximum likelihood estimation (MLE), we proposed to use maximum a posteriori (MAP) as the estimation criterion. The proposed algorithm, called 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 outgoing other algorithms, such as maximum a posteriori linear regression (MAPLR) algorithm and maximum likelihood linear regression (MLLR) algorithm etc..

【基金】 国家自然科学基金资助项目(60272044)
  • 【分类号】TP391.42
  • 【被引频次】11
  • 【下载频次】170
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