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摘要: 研究了将自适应领域的最大似然线性回归(Maximum likelihood linear regression, MLLR)变换矩阵作为特征进行文本无关的说话人识别算法. 本文引入了基于统一背景模型的MLLRSV-SVM说话人识别算法, 并在此基础上进行高层音素聚类以进一步提高识别性能. 在采用多种信道补偿技术后, 在NIST SRE 2006年1训练语段-1测试语段同信道和跨信道数据库上, 基于MLLR特征的系统与其他最好的系统性能接近并有很强的互补性, 经过简单线性融合可以极大提高识别性能.Abstract: This paper uses the maximum likelihood linear regression (MLLR) as feature for text-independent speaker recognition algorithm. We introduce a universal background model (UBM) based MLLRSV-SVM algorithm first, and then extend the algorithm to multi-class for improvement. After channel compensation, in terms of the NIST 2006 SRE 1conv4w-1conv4w/mic corpus, the MLLR based system is comparable with and complementary of the state of the art systems. The performance is greatly improved by simply linear fusion.
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