Entropy analysis of i-vector feature spaces in duration-sensitive speaker recognition

A. Nautsch, C. Rathgeb, R. Saeidi, C. Busch
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引用次数: 11

Abstract

The vast majority of speaker recognition cross-entropy evaluations are focused on score domain. By examining the generalized relative distance between genuine and impostor sub-spaces, biometric characteristics become comparable to other authentication approaches. In this paper we demonstrate that the i-vector feature space's biometric information measured by relative entropy is comparable to e.g., knowledge-based mechanisms or face recognition. Examining NIST SRE 2004-2010 corpora, short samples of e.g, 5 seconds duration, comprise already 127 bits in a text-independent scenario. Further, the vast majority of short samples does not fall below 50% of the biometric information of samples having a duration of more than 40 seconds. The generalized i-vector feature space entropy of long samples corresponds to 182.1 bits, and the highest lower entropy bound of a subject was observed at 471.6 bits.
时长敏感说话人识别中i向量特征空间的熵分析
绝大多数的说话人识别交叉熵评价都集中在分数域。通过检查真实和冒名顶替子空间之间的广义相对距离,生物特征可以与其他认证方法相比较。在本文中,我们证明了由相对熵测量的i向量特征空间的生物特征信息可与例如基于知识的机制或人脸识别相比较。检查NIST SRE 2004-2010语料库,例如持续时间为5秒的短样本,在独立于文本的场景中已经包含127位。此外,绝大多数短样本不低于持续时间超过40秒的样本的生物特征信息的50%。长样本的广义i向量特征空间熵对应于182.1 bits,受试者的最高下熵界为471.6 bits。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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