Height Estimation from Speech Signals using i-vectors and Least-Squares Support Vector Regression

被引：0
|
作者：
Poorjam, Amir Hossein [1 ]
Bahari, Mohamad Hasan [1 ]
Vasilakakis, Vasileios [2 ]
Van hamme, Hugo [1 ]
机构：
[1] Katholieke Univ Leuven, Ctr Proc Speech & Images, Louvain, Belgium
[2] Polytech Univ Turin, Turin, Italy
来源：
2015 38TH INTERNATIONAL CONFERENCE ON TELECOMMUNICATIONS AND SIGNAL PROCESSING (TSP) | 2015年
关键词：
Artificial Neural Networks; i-vector; Least-squares Support Vector Regression; Speaker Height Estimation; SPEAKER HEIGHT;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
This paper proposes a novel approach for automatic speaker height estimation based on the i-vector framework. In this method, each utterance is modeled by its corresponding i-vector. Then artificial neural networks (ANNs) and least-squares support vector regression (LSSVR) are employed to estimate the height of a speaker from a given utterance. The proposed method is trained and tested on the telephone speech signals of National Institute of Standards and Technology (NIST)2008 and 2010 Speaker Recognition Evaluation (SRE) corpora respectively. Evaluation results show the effectiveness of the proposed method in speaker height estimation.
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页数：5
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