Partial Least Squares Based Total Variability Space Modeling for I-Vector Speaker Verification  

作　　者：CHEN Chen HAN Jiqing 

机构地区：[1]School of Computer Science and Technology, Harbin Institute of Technology

出　　处：《Chinese Journal of Electronics》2018年第6期1229-1233,共5页电子学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(No.61471145,No.91120303)

摘　　要：As an effective and low-dimension representation for speech utterances with different lengths,i-vector method has drawn considerable attentions in speaker verification. Training a Total variability space(TVS) is one of the key parts in the i-vector method. However, the traditional training method only explores the relationship between different mean supervectors, ignoring priori category information of speakers, which results in a lack of discrimination. In the proposed method, a discriminative TVS based on Partial least squares(PLS) is estimated, in which both the correlation of intra-class and the distinction of inter-class are fully utilized due to using speaker labels, and the proposed method can achieve a better performance.As an effective and low-dimension representation for speech utterances with different lengths, i-vector method has drawn considerable attentions in speaker verification. Training a Total variability space （TVS） is one of the key parts in the i-vector method. However, the traditional training method only explores the relationship between different mean supervectors, ignoring priori category information of speakers, which results in a lack of discrimination. In the proposed method, a discriminative TVS based on Partial least squares （PLS） is estimated, in which both the correlation of intra-class and the distinction of inter-class are fully utilized due to using speaker labels, and the proposed method can achieve a better performance.

关 键 词：Speaker verification I-vector Total variability space Partial least squares 

分 类 号：TN912.34[电子电信—通信与信息系统]