  DeepMine is a speech database in Persian and English designed to build and
evaluate text-dependent, text-prompted, and text-independent speaker
verification, as well as Persian speech recognition systems. It contains more
than 1850 speakers and 540 thousand recordings overall, more than 480 hours of
speech are transcribed. It is the first public large-scale speaker verification
database in Persian, the largest public text-dependent and text-prompted
speaker verification database in English, and the largest public evaluation
dataset for text-independent speaker verification. It has a good coverage of
age, gender, and accents. We provide several evaluation protocols for each part
of the database to allow for research on different aspects of speaker
verification. We also provide the results of several experiments that can be
considered as baselines: HMM-based i-vectors for text-dependent speaker
verification, and HMM-based as well as state-of-the-art deep neural network
based ASR. We demonstrate that the database can serve for training robust ASR
models.
