Reflection of Conditional Independence Structure to Noise Variability for Noise Robust Text Dependent Speaker Verification

Abstract

In the field of speaker verification (SV), the development of noise-robust systems is a challenge for their deployment in real-world environments. Noise variability compensation is a common strategy for increasing the robustness to noise variations. The performance of noise compensation depends on how well the noise variability, which is inherent in within-class variability, is estimated. However, to date, there is no information about the true noise variability that could reduce the gap between the empirical and true statistics. Most studies assume that true noise covariates are independent. This study aims to demonstrate the assumption that true noise variability has a conditional independence structure rather than an independence structure. This assumption was motivated by our previous findings, which revealed that optimal within-class variability has a conditional independence structure in text-dependent speaker verification (TD-SV) in clean environments. This indicates that the optima of all the variabilities in within-class variability, except noise variability, has a conditional independence structure; however, it is unknown whether this is also true for optimal noise variability. Our assumption was supported by the experimental results obtained under noisy TD-SV trials using systems built with graphical least absolute shrinking and selection operator-based probabilistic linear discriminant analysis, which achieved up to 10% relative equal error rate improvements.

Keywords

• Background noise
• conditional independence
• graphical least absolute shrinking and selection operator (GLASSO)
• probabilistic linear discriminant analysis (PLDA)
• text-dependent speaker verification