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Data augmentation is commonly used for generating additional data from theavailable training data to achieve a robust estimation of the parameters ofcomplex models like the one for speaker verification (SV), especially forunder-resourced applications. SV involves training speaker-independent (SI)models and speaker-dependent models where speakers are represented by modelsderived from an SI model using the training data for the particular speakerduring the enrollment phase. While data augmentation for training SI models iswell studied, data augmentation for speaker enrollment is rarely explored. Inthis paper, we propose the use of data augmentation methods for generatingextra data to empower speaker enrollment. Each data augmentation methodgenerates a new data set. Two strategies of using the data sets are explored:the first one is to training separate systems and fuses them at the score leveland the other is to conduct multi-conditional training. Furthermore, we studythe effect of data augmentation under noisy conditions. Experiments areperformed on RedDots challenge 2016 database, and the results validate theeffectiveness of the proposed methods.

Data augmentation enhanced speaker enrollment for text-dependent speaker verification