A Generalized Framework for Domain Adaptation of PLDA in Speaker Recognition
This work addresses domain mismatch issues in speaker recognition systems, offering incremental improvements for practical applications.
The paper tackles domain adaptation for PLDA in speaker recognition by introducing a generalized framework with two new techniques: correlation-alignment-based interpolation and covariance regularization, resulting in a 30.5% reduction in minCprimary compared to out-of-domain models and a 5.5% improvement over conventional methods.
This paper proposes a generalized framework for domain adaptation of Probabilistic Linear Discriminant Analysis (PLDA) in speaker recognition. It not only includes several existing supervised and unsupervised domain adaptation methods but also makes possible more flexible usage of available data in different domains. In particular, we introduce here the two new techniques described below. (1) Correlation-alignment-based interpolation and (2) covariance regularization. The proposed correlation-alignment-based interpolation method decreases minCprimary up to 30.5% as compared with that from an out-of-domain PLDA model before adaptation, and minCprimary is also 5.5% lower than with a conventional linear interpolation method with optimal interpolation weights. Further, the proposed regularization technique ensures robustness in interpolations w.r.t. varying interpolation weights, which in practice is essential.