  Research on speaker recognition is extending to address the vulnerability in
the wild conditions, among which genre mismatch is perhaps the most
challenging, for instance, enrollment with reading speech while testing with
conversational or singing audio. This mismatch leads to complex and composite
inter-session variations, both intrinsic (i.e., speaking style, physiological
status) and extrinsic (i.e., recording device, background noise).
Unfortunately, the few existing multi-genre corpora are not only limited in
size but are also recorded under controlled conditions, which cannot support
conclusive research on the multi-genre problem. In this work, we firstly
publish CN-Celeb, a large-scale multi-genre corpus that includes in-the-wild
speech utterances of 3,000 speakers in 11 different genres. Secondly, using
this dataset, we conduct a comprehensive study on the multi-genre phenomenon,
in particular the impact of the multi-genre challenge on speaker recognition
and the performance gain when the new dataset is used to conduct multi-genre
training.
