Long Term Examination of Intra-Session and Inter-Session Speaker Variability

被引：0
|
作者：
Lawson, A. D. [1 ]
Stauffer, A. R. [1 ]
Smolenski, B. Y. [1 ]
Pokines, B. B. [2 ]
Leonard, M. [3 ]
Cupples, E. J. [1 ]
机构：
[1] RADC Inc, Rome, NY 13441 USA
[2] Oasis Syst, Lexington, MA USA
[3] Univ Texas Dallas, Dallas, TX USA
来源：
INTERSPEECH 2009: 10TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2009, VOLS 1-5 | 2009年
关键词：
session variability; speaker recognition; speaker variability analysis; conversation analysis;
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：
Session variability in speaker recognition is a well recognized phenomena, but poorly understood largely due to a dearth of robust longitudinal data. The current study uses a large, long-term speaker database to quantify both speaker variability changes within a conversation and the impact of speaker variability changes over the long term (3 years). Results demonstrate that I) change in accuracy over the course of a conversation is statistically very robust and 2) that the aging effect over three years is statistically negligible. Finally we demonstrate that voice change during the course of a conversation is, in large part, comparable across sessions.
引用
收藏
页码：2867 / +
页数：2
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