Speaker Recognition in Orthogonal Complement of Time Session Variability Subspace
Speaker Recognition in Orthogonal Complement of Time Session Variability Subspace
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DOI：
10.1007/978-3-319-92231-7_11
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发表时间：
2018-06
期刊：
影响因子：
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通讯作者：
S. Tsuge;S. Kuroiwa
中科院分区：
文献类型：
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作者：
S. Tsuge;S. Kuroiwa
注册数据和识别数据之间的时间会话可变性降低了说话人识别性能。因此，说话人识别技术中的一个重要问题就是语音的时变特性。在本文中，我们提出了一个鲁棒的说话人识别方法的时间会话变化。所提出的方法估计时间会话变化子空间。然后，该方法在时间会话变化子空间的正交补上进行说话人识别。此外，我们将线性判别分析方法到所提出的方法。为了评估所提出的方法，我们进行了说话人识别实验。实验结果表明，该方法提高了基线说话人识别性能。
A time session variability between the enrollment data and the recognized data degrades speaker recognition performance. Hence, the time session variability is one of the most important issues in the speaker recognition technology. In this paper, we propose a robust speaker recognition method for the time session variability. The proposed method estimates a time session variability subspace. Then, the proposed method carries out the speaker recognition in the orthogonal complement of the time session variability subspace. In addition, we incorporate a linear discriminant analysis method into the proposed method. In order to evaluate the proposed method, we conducted a speaker identification experiment. Experimental results show that the proposed method improves speaker identification performance of baseline.