  Data augmentation is commonly used to help build a robust speaker
verification system, especially in limited-resource case. However, conventional
data augmentation methods usually focus on the diversity of acoustic
environment, leaving the lexicon variation neglected. For text dependent
speaker verification tasks, it's well-known that preparing training data with
the target transcript is the most effectual approach to build a well-performing
system, however collecting such data is time-consuming and expensive. In this
work, we propose a unit selection synthesis based data augmentation method to
leverage the abundant text-independent data resources. In this approach
text-independent speeches of each speaker are firstly broke up to speech
segments each contains one phone unit. Then segments that contain phonetics in
the target transcript are selected to produce a speech with the target
transcript by concatenating them in turn. Experiments are carried out on the
AISHELL Speaker Verification Challenge 2019 database, the results and analysis
shows that our proposed method can boost the system performance significantly.
