结合因子分析技术的鲁棒说话人识别系统

Shaghayegh Reza, Tahereh Emami Azadi, J. Kabudian, Y. Shekofteh
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引用次数: 0

摘要

在本文中,我们实现了最先进的基于因子分析的方法,并融合了它们的分数来获得一个信道鲁棒的说话人识别系统。这两种方法分别是联合因子分析(JFA)和i-Vector,它们定义了低维扬声器和通道相关空间。对于分数融合,我们提出了一种不需要训练步骤的简单权值计算方法。我们在两个条件下试验我们的方法;1)信道匹配训练和测试信道(训练阶段的电话/测试阶段的电话)任务和2)信道不匹配条件(电话训练阶段/麦克风,GSM和VOIP测试阶段)任务。我们的策略优于最先进的基于GMM-UBM的系统。与基于标准GMM-UBM的方法相比,我们在信道依赖和信道独立条件下都获得了超过4%的绝对EER改善。仿真结果表明,基于i-Vector和JFA的组合系统的性能优于所有实现方法。
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A robust speaker recognition system combining factor analysis techniques
In this paper we implement state of the art factor analysis based methods and fused their scores to gain a channel robust speaker recognition system. These two methods are joint factor analysis (JFA) and i-Vector which define low-dimensional speaker and channel dependent spaces. For score fusion we propose a simple weight computation without training step. We experiment our method on two conditions; 1) in channel matched training and test channel (telephone in training phase/telephone in test phase) task and 2) the channel mismatched condition (telephone training phase/microphone, GSM and VOIP in test phase) task. Our strategies outperform a state-of-the-art GMM-UBM based system. We obtained more than 4% absolute EER improvement for both channel dependent and channel independent condition compared to the standard GMM-UBM based method. Simulation also results that the combined system based on i-Vector and JFA gives better performance than all implemented method.
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