Utilization of unlabeled development data for speaker verification

Gang Liu, Chengzhu Yu, Navid Shokouhi, Abhinav Misra, Hua Xing, John H. L. Hansen
2014 2014 IEEE Spoken Language Technology Workshop (SLT)  
State-of-the-art speaker verification systems model speaker identity by mapping i-Vectors onto a probabilistic linear discriminant analysis (PLDA) space. Compared to other modeling approaches (such as cosine distance scoring), PLDA provides a more efficient mechanism to separate speaker information from other sources of undesired variabilities and offers superior speaker verification performance. Unfortunately, this efficiency is obtained at the cost of a required large corpus of labeled
more » ... ment data, which is too expensive/unrealistic in many cases. This study investigates a potential solution to resolve this challenge by effectively utilizing unlabeled development data with universal imposter clustering. The proposed method offers +21.9% and +34.6% relative gains versus the baseline system on two public available corpora, respectively. This significant improvement proves the effectiveness of the proposed method.
doi:10.1109/slt.2014.7078611 dblp:conf/slt/LiuYSMXH14 fatcat:xigylza7izf53kzblgx7s6k6sm