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摘要: 在说话人识别中，有效的识别方法是核心.近年来，基于总变化因子分析（i-vector）方法成为了说话人识别领域的主流，其中总变化因子空间的估计是整个算法的关键.本文结合常规的因子分析方法提出一种新的总变化因子空间估计算法，即通用背景—联合估计（Universal background-joint estimation algorithm，UB-JE）算法.首先，根据高斯混合—通用背景模型（Gaussian mixture model-universal background model，GMM-UBM）思想提出总变化矩阵通用背景（UB）算法；其次，根据因子分析理论结合相关文献提出了一种总变化矩阵联合估计（JE）算法；最后，将两种算法相结合得到通用背景—联合估计（UB-JE）算法.采用TIMIT和MDSVC语音数据库，结合i-vector方法将所提的算法与传统算法进行对比实验.结果显示，等错误率（Equal error rate，EER）和最小检测代价函数（Minimum detection cost function，MinDCF）分别提升了8.3%与6.9%，所提方法能够提升i-vector方法的性能.
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关键词:
- 总变化因子分析 /
- 总变化因子空间 /
- 通用背景—联合估计算法 /
- 说话人识别
Abstract: In the speaker recognition, the effective identification method is the core. In recent years, i-vector method has become the mainstream in the field of speaker recognition, and estimation of the total variation factor space is the key of whole algorithm. In this paper, we propose a new algorithm for total variation factor space estimation named UB-JE, which is combined with conventional factor analysis method. Firstly, the universal background algorithm of total variation matrix is proposed according to Gaussian mixture model-universal background model (GMM-UBM). Secondly, the joint estimation algorithm of total variation matrix is proposed according to the factor analysis theory and related works. Finally, the two algorithms are combined to get the universal background-joint estimation algorithm (UB-JE). TIMIT and MDSVC corpus are adopted in the experiment to compare the proposed algorithm with the traditional algorithm. Experimental results show that the equal error rate (EER) and the minimum detection cost function (MinDCF) are improved by 8.3% and 6.9%, respectively. The proposed method can improve the performance of i-vector method.1) 本文责任编委 吴玺宏 -
图 3 总变化因子的常规估计算法和UB算法(虚线框)比较
Fig. 3 Comparison of conventional estimation algorithm of total variation factor with UB (dashed frame)
图 4 通用背景-联合估计算法(虚线框)
Fig. 4 Diagram of universal background-joint estimation algorithm (dashed frame)
图 5 不同语音库中各算法性能对比
Fig. 5 Performance comparison of algorithms on different speech corpus
图 6 不同算法在四种语音库中的性能对比
Fig. 6 Performance comparison of different algorithms on four speech corpus
表 1 实验所用语音库
Table 1 The corpus used in the experiment
类型 TIMIT MDSVC MDSVC长句 male female male female UBM 3 860 1 620 2 808 2376 136 T 3 860 1 620 2 808 2 376 136 训练GSV 630 270 1 150 850 1 500 1 500 测试 70 30 92 68 120 120 
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表 2 MinDCF10参数设定
Table 2 MinDCF10 parameter setting
$C_{\rm Miss} $ $C_{\rm FalseAlarm} $ $P_{\rm Target} $ 1 1 0.001
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表 3 GMM-UBM、传统算法估计$T$、本文所提出算法估计$T$以及PLDA在TIMIT语音库上的性能对比
Table 3 Performance comparison of GMM-UBM, the traditional algorithm to estimate $T$, the proposed algorithms to estimate $T$, and the PLDA on TIMIT corpora
算法 EER (%) MinDCF10 GMM-UBM 6.26 0.076 传统算法估计$T$ 4.76 0.025 通用背景估计$T$ 4.28 0.021 联合估计$T$ 4.01 0.020 通用背景-联合估计$T$ 3.76 (21 %) 0.019 (24 %) PLDA 3.94 0.022
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表 4 GMM-UBM、传统算法估计$T$、本文所提出算法估计$T$以及PLDA在MDSVC语音库上的性能对比
Table 4 Performance comparison of GMM-UBM, the traditional algorithm to estimate $T$, the proposed algorithms to estimate $T$, and the PLDA on MDSVC corpora
算法 EER (%) MinDCF10 GMM-UBM 7.57 0.072 传统算法估计$T$ 4.96 0.027 通用背景估计$T$ 4.92 0.026 联合估计$T$ 4.71 0.024 通用背景-联合估计$T$ 4.67 (5.8 %) 0.023 (14.8 %) PLDA 4.67 0.024
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表 5 GMM-UBM、传统算法估计$T$、本文所提出算法估计$T$以及PLDA在TIMIT + MDSVC语音库上的性能对比
Table 5 Performance comparison of GMM-UBM, the traditional algorithm to estimate $T$, the proposed algorithms to estimate $T$, and the PLDA on TIMIT mixed MDSVC corpora
算法 EER (%) MinDCF10 GMM-UBM 8.33 0.071 传统算法估计$T$ 5.41 0.029 通用背景估计$T$ 5.19 0.028 联合估计$T$ 5.11 0.028 通用背景-联合估计$T$ 4.96 (8.3 %) 0.027 (6.9 %) PLDA 5.01 0.025
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表 6 GMM-UBM、传统算法估计$T$、本文所提出算法估计$T$以及PLDA在MDSVC长句语音库上的性能对比
Table 6 Performance comparison of GMM-UBM, the traditional algorithm to estimate $T$, the proposed algorithms to estimate $T$, and the PLDA on MDSVC long sentence corpora
算法 EER (%) MinDCF10 GMM-UBM 6.58 0.067 传统算法估计$T$ 4.45 0.022 通用背景估计$T$ 3.96 0.021 联合估计$T$ 3.73 0.021 通用背景-联合估计$T$ 3.72 (16.40 %) 0.020 (9.09 %) PLDA 3.88 0.021
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表 7 通用背景-联合估计算法在不同语音库中的性能对比
Table 7 Performance comparison of universal background-joint estimation algorithm on different speech corpus
语音库 EER (%) MinDCF10 TIMIT 3.76 0.019 MDSVC 4.67 0.023 TIMIT + MDSVC 4.96 0.027 MDSVC长句 3.72 0.020
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