摘要
随着说话人模型数量的增加,说话人识别系统的识别速度下降,不能满足实时性要求。针对这个问题,提出了基于分层识别模型的快速说话人识别方法。将变分法求解的KL散度的近似值作为模型间的相似性度量准则,并设计了说话人模型聚类的方法。结果表明,本文方法能够保证说话人模型聚类结果的有效性,在系统识别率损失很小的情况下,使系统的识别速度得到大幅度提升。
As the number of speaker models increases,the recognition speed of the speaker recognition system decreases,thus it cannot meet real-time requirement.To solve this problem,we propose a fast speaker recognition method based on hierarchical recognition model.The approximate value of the KL divergence solved by the variational method is used as the similarity measure between speaker models and a speaker model clustering method is designed.Experimental results show that the proposed method can ensure the validity of speaker model clustering results and improve the recognition speed of the system greatly while maintaining a small system recognition rate loss.
作者
茅正冲
涂文辉
MAO Zheng-chong;TU Wen hui(Key Laboratory of Advanced Process Control for Light Industry Ministry of Education,Jiangnan University,Wuxi 214122,China)
出处
《计算机工程与科学》
CSCD
北大核心
2018年第7期1244-1249,共6页
Computer Engineering & Science
基金
国家自然科学基金(60973095)
江苏省自然科学基金(BK20131107)
关键词
高斯混合模型
说话人识别
KL散度
模型聚类
Gauss mixture model
speaker recognition
KL divergence
model clustering
