Robust Speaker Identification using Independent Component Analysis
Robust Speaker Identification using Independent Component Analysis
复制标题
使用独立分量分析进行稳健的说话人识别
DOI：
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复制
发表时间：
2000
期刊：
影响因子：
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通讯作者：
Yung
中科院分区：
文献类型：
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作者：
Gil;Yung
提出了一种基于独立分量分析（伊卡）的特征参数变换方法，用于说话人识别.该方法假设各种信道条件下的语音倒谱向量是由一些特征函数与随机信道噪声的线性组合构成的，并使用伊卡将它们转换为新的向量。合成的矢量空间可以强调重复的说话人信息，并抑制随机信道失真。实验结果表明，该变换方法对提高说话人识别系统的性能是有效的。
This paper proposes feature parameter transformation method using independent component analysis (ICA) for speaker identification. The proposed method assumes that the cepstral vectors from various channel-conditioned speech are constructed by a linear combination of some characteristic functions with random channel noise added, and transforms them into new vectors using ICA. The resultant vector space can give emphasis to the repetitive speaker information and suppress the random channel distortions. Experimental results show that the transformation method is effective for the improvement of speaker identification system.