Speaker Recognition Using Discriminant Neighborhood Embedding
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摘要: 该文提出一种基于判别邻域嵌入(DNE)算法的说话人识别。判别邻域嵌入算法作为流形学习方法的一种，可以通过构建邻接图获取数据的局部邻域结构信息；同时该算法可以充分利用类间判别信息，具有更强的判别能力。在美国国家标准技术研究院2010年说话人识别评测(NIST SRE 2010)电话-电话核心测试集上的实验结果表明了该算法的有效性。Abstract: Discriminant Neighborhood Embedding (DNE) algorithm is introduced into the speaker recognition system. DNE is a manifold learning approach and aims at preserving the local neighborhood structure on the data manifold. As well, DNE has much more power in discrimination by sufficiently using the between-class discriminant information. The experimental results on the telephone-telephone core condition of the NIST 2010 Speaker Recognition Evaluation (SRE) dataset indicate the effectiveness of DNE algorithm.
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表 1 NIST SRE 2010电话-电话测试集上DNE和NPE的EER和minDCF比较(无信道补偿)
系统 男声 女声 EER(%) minDCF EER(%) minDCF NPE 5.76 0.0575 6.98 0.0744 DNE 5.28 0.0544 6.35 0.0683 
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表 2 NIST SRE 2010电话-电话测试集上DNE和NPE的EER和minDCF比较(LDA信道补偿)
系统 男声 女声 EER(%) minDCF EER(%) minDCF NPE+LDA 4.71 0.0492 6.11 0.0633 DNE+LDA 4.19 0.0453 5.57 0.0604
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表 3 NIST SRE 2010电话-电话测试集上DNE和NPE的EER和minDCF比较(WCCN信道补偿)
系统 男声 女声 EER(%) minDCF EER(%) minDCF NPE+WCCN 5.07 0.0512 6.49 0.0677 DNE+WCCN 4.59 0.0478 5.83 0.0617
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表 4 NIST SRE 2010电话-电话测试集上DNE和NPE的EER和minDCF比较(LDA+WCCN信道补偿)
系统 男声 女声 EER(%) minDCF EER(%) minDCF NPE+LDA+WCCN 4.41 0.0476 5.72 0.0584 DNE+LDA+WCCN 4.15 0.0434 5.24 0.0553
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表 5 NIST SRE 2010电话-电话测试集上DNE和PLDA的EER和minDCF比较
系统 男声 女声 EER(%) minDCF EER(%) minDCF DNE+LDA+WCCN 4.15 0.0434 5.24 0.0553 PLDA 4.12 0.0428 5.37 0.0532
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