文件名称:Spedaker_Adapting_in_Speech_recognizing
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：自适应技术在近年来得到越来越多的重视，其中应用广泛的包括,-.、,//0，该技术利用少量特定
人数据就可以调整码本，快速地提升识别性能，它要求原始的码本有很好的说话人无关性。本文介绍了结合
,//0 自适应的说话人自适应训练（1234536 -74289:3 649<9<=，以下简称1- ）算法，这种方法将每个说话人码本
视为说话人无关码本经过线性变换的结果，在此基础上训练的说话人无关码本更有效剔除了说话人相关信
息，因此在说话人自适应中时能根据特定数据调整更好地逼近说话人特性，从而有更好的性能表现。-Introduced the term network based maximum likelihood linear regression unsupervised adaptive algorithm, and an improved. According to decode the received word net estimated transformation parameters, the word error rate of net potential is far less than the recognition results, it can make parameter estimation more accurate. A major drawback is that the traditional calculation enormously difficult practical, this paper presents two improved technology: 1 compression using word posterior probability network 2 time information using the word limit state statistic calculation. Experimental determination of the relative error rate than traditional down.
人数据就可以调整码本，快速地提升识别性能，它要求原始的码本有很好的说话人无关性。本文介绍了结合
,//0 自适应的说话人自适应训练（1234536 -74289:3 649<9<=，以下简称1- ）算法，这种方法将每个说话人码本
视为说话人无关码本经过线性变换的结果，在此基础上训练的说话人无关码本更有效剔除了说话人相关信
息，因此在说话人自适应中时能根据特定数据调整更好地逼近说话人特性，从而有更好的性能表现。-Introduced the term network based maximum likelihood linear regression unsupervised adaptive algorithm, and an improved. According to decode the received word net estimated transformation parameters, the word error rate of net potential is far less than the recognition results, it can make parameter estimation more accurate. A major drawback is that the traditional calculation enormously difficult practical, this paper presents two improved technology: 1 compression using word posterior probability network 2 time information using the word limit state statistic calculation. Experimental determination of the relative error rate than traditional down.
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说话人自适应训练方法在连续语音识别中的应用.pdf