A Quality Measure Method Using Gaussian Mixture Models and Divergence Measure for Speaker Identification

被引：0

作者：

Zheng, Rong ^{[1
]}

Zhang, Shuwu ^{[1
]}

Xu, Bo ^{[1
]}

机构：

[1] Chinese Acad Sci, Inst Automat, Beijing, Peoples R China

来源：

INTERSPEECH 2006 AND 9TH INTERNATIONAL CONFERENCE ON SPOKEN LANGUAGE PROCESSING, VOLS 1-5 | 2006年

关键词：

speaker identification; quality measure; Gaussian mixture density; divergence measure;

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：

Previous work has demonstrated the promise of frame-level quality measure methods to robust speaker recognition. This paper explores the issues involved in applying soft estimates to quality measures as weighting factors in score computation. A quality measure algorithm using Gaussian mixture density and Jensen divergence measure is presented for traditional GMM-UBM scoring mechanism. Derivation and validation of the quality measurement are reported in this paper. We investigate the usefulness of different feature processing, different GMM-based quality models and incoporation of divergence measure for quality estimation. Comparison experiments performed on the NIST1999 SRE corpus show the effectiveness of the proposed method.

引用

页码：2094 / 2097

页数：4

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