Open AccessCross‐domain variation compensation for robust speaker verification
Author(s) -
Huang Houjun,
Zhou Ruohua,
Yan Yonghong
Publication year - 2015
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2015.1701
Subject(s) - speaker verification , computer science , domain (mathematical analysis) , compensation (psychology) , speaker recognition , variation (astronomy) , speech recognition , artificial intelligence , probabilistic logic , pattern recognition (psychology) , frequency domain , mathematics , computer vision , psychology , mathematical analysis , physics , astrophysics , psychoanalysis
Recent studies have shown that when state‐of‐the‐art probabilistic linear discriminant analysis (PLDA) speaker verification systems are developed with out‐domain data, the mismatch between development data and evaluation data significantly degrades speaker verification performance. An unsupervised cross‐domain variation compensation (CDVC) approach to compensate the domain mismatch is proposed. This approach is based on the assumption that the inter‐domain variability is an additive factor with normal distribution in the i ‐vector space. The effect of the approach on the domain adaption challenge of the JHU 2013 speaker recognition workshop is tested. Applying the CDVC approach on evaluation i ‐vectors, the out‐domain PLDA system achieves a relative performance improvement of 61.9% in equal error rate.