Speaker identification using fuzzy i-vector tree

被引：3
|
作者：
Galka, Jakub [1 ]
Jaciow, Pawel [1 ]
机构：
[1] AGH Univ Sci & Technol, Dept Elect, Krakow, Poland
来源：
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS | 2019年 / 37卷 / 04期
关键词：
speaker recognition; fast identification; i-vector; fuzzy clustering; decision trees; RECOGNITION;
D O I：
10.3233/JIFS-181359
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we propose a new method for concurrent accuracy and computational efficiency optimization using a fuzzy clusters tree for i-vector speaker identification. The design assumptions and an algorithm for a new type of fuzzy i-vector tree construction were introduced. The obtained solution was evaluated using the NIST 2014 i-Vector Speaker Recognition Machine Learning Challenge dataset. A 15% relative equal error rate reduction for a 74% reduction in computation time was achieved when compared to the baseline with only a 5.5% relative identification rate loss for discussed tree configurations.
引用
收藏
页码：4937 / 4949
页数：13
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