  Data augmentation (DA) has played a pivotal role in the success of deep
speaker recognition. Current DA techniques primarily focus on
speaker-preserving augmentation, which does not change the speaker trait of the
speech and does not create new speakers. Recent research has shed light on the
potential of speaker augmentation, which generates new speakers to enrich the
training dataset. In this study, we delve into two speaker augmentation
approaches: speed perturbation (SP) and vocal tract length perturbation (VTLP).
Despite the empirical utilization of both methods, a comprehensive
investigation into their efficacy is lacking. Our study, conducted using two
public datasets, VoxCeleb and CN-Celeb, revealed that both SP and VTLP are
proficient at generating new speakers, leading to significant performance
improvements in speaker recognition. Furthermore, they exhibit distinct
properties in sensitivity to perturbation factors and data complexity, hinting
at the potential benefits of their fusion. Our research underscores the
substantial potential of speaker augmentation, highlighting the importance of
in-depth exploration and analysis.
