  Recently, researchers set an ambitious goal of conducting speaker recognition
in unconstrained conditions where the variations on ambient, channel and
emotion could be arbitrary. However, most publicly available datasets are
collected under constrained environments, i.e., with little noise and limited
channel variation. These datasets tend to deliver over optimistic performance
and do not meet the request of research on speaker recognition in unconstrained
conditions. In this paper, we present CN-Celeb, a large-scale speaker
recognition dataset collected `in the wild'. This dataset contains more than
130,000 utterances from 1,000 Chinese celebrities, and covers 11 different
genres in real world. Experiments conducted with two state-of-the-art speaker
recognition approaches (i-vector and x-vector) show that the performance on
CN-Celeb is far inferior to the one obtained on VoxCeleb, a widely used speaker
recognition dataset. This result demonstrates that in real-life conditions, the
performance of existing techniques might be much worse than it was thought. Our
database is free for researchers and can be downloaded from
http://project.cslt.org.
