Investigating the Effect of Data Partitioning for GMM Supervector Based Speaker Verification

Abstract—GMM supervectors are among the most popular feature sets used in SVM-based text-independent speaker verification systems. Most of the studies use only a single supervector to represent speaker characteristics, against a set of background samples. An alternative would be to divide the total training duration into smaller pieces to increase the number of supervectors for training the minority (speaker) class. Similarly, total test duration could also be partitioned, letting the final verification be made by majority voting over decisions on smaller durations. We explore the performance of speaker verification systems in terms of EER and minDCF by breaking down the input sequence into durations of 4 minutes, 1 minute and 10 seconds. We try different training/test data amounts to investigate the generalizability of this approach. Working on the CSLU Speaker Recognition Dataset, we show that the lowest error rates are obtained when the training supervector representative duration is set equal to that of the test samples. I