Adaptive Individual Background Model for Speaker Verification

被引：0
|
作者：
Bar-Yosef, Yossi [1 ]
Bistritz, Yuval [1 ]
机构：
[1] Tel Aviv Univ, Dept Elect Engn, IL-69978 Tel Aviv, Israel
来源：
INTERSPEECH 2009: 10TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2009, VOLS 1-5 | 2009年
关键词：
Model adaptation; Gaussian Mixture Models; Kullback-Leibler divergence; speaker verification; cohort selection; score normalization;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Most techniques for speaker verification today use Gaussian Mixture Models (GMMs) and make the decision by comparing the likelihood of the speaker model to the likelihood of a universal background model (UBM). The paper proposes to replace the UBM by an individual background model (IBM) that is generated for each speaker. The IBM is created using the K-nearest cohort models and the UBM by a simple new adaptation algorithm. The new GMM-IBM speaker verification system can also be combined with various score normalization techniques that have been proposed to increase the robustness of the GMM-UBM system. Comparative experiments were held on the NIST-2004-SRE database with a plain system setting (without score normalization) and also with the combination of adaptive test normalization (ATnorm). Results indicated that the proposed GMM-IBM system outperforms a comparable GMM-UBM system.
引用
收藏
页码：1279 / 1282
页数：4
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