基于矢量泰勒级数的模型自适应算法  

Model Adaptation Algorithm Using Vector Taylor Series

作　　者：吕勇[1] 吴镇扬[1] 

机构地区：[1]东南大学信息科学与工程学院,南京210096

出　　处：《电子与信息学报》2010年第1期107-111,共5页Journal of Electronics & Information Technology

基　　金：国家973计划项目(2002CB312102);国家自然科学基金(60971098)资助课题

摘　　要：在实际环境中,由于测试环境与训练环境的不匹配,语音识别系统的性能会急剧恶化。模型自适应算法是减小环境失配影响的有效方法之一,它通过测试环境下的少量自适应数据,将HMM模型的参数变换到测试环境下。该文将矢量泰勒级数用于模型自适应,同时对HMM模型的均值向量和协方差矩阵进行变换,使其与实际环境相匹配。实验证明,该文算法优于MLLR算法和基于矢量泰勒级数的特征补偿算法,在低信噪比环境中性能提高尤为明显。In actual environments the performance of speech recognition system may be degraded significantly because of the mismatch between the training and testing conditions. Model adaptation is an efficient approach that could reduce this mismatch, which adapts model parameters to new conditions by some adaptation data. In this paper, a new model adaptation using vector Taylor series is presented, which adapts the mean vector and covariance matrix of hidden Markov model. The experimental results show that the proposed algorithm is more effective them MLLR and the feature compensation algorithm based on vector Taylor series in various environments, especially in low signal-to-noise ratio environments.

关 键 词：语音识别 模型自适应 矢量泰勒级数 隐马尔可夫模型 

分 类 号：TN912.34[电子电信—通信与信息系统]