Utilization of unlabeled development data for speaker verification

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...

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Bibliographic Details
Published in2014 IEEE Spoken Language Technology Workshop (SLT) pp. 418 - 423
Main Authors Gang Liu, Chengzhu Yu, Shokouhi, Navid, Misra, Abhinav, Hua Xing, Hansen, John H. L.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2014
Subjects
Clustering
Clustering algorithms
Clustering methods
Data models
i-Vector
NIST
PLDA
Speaker recognition
Speaker verification
Speech
Universal imposter clustering
Vectors
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Summary: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 development 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