  Trivial events are ubiquitous in human to human conversations, e.g., cough,
laugh and sniff. Compared to regular speech, these trivial events are usually
short and unclear, thus generally regarded as not speaker discriminative and so
are largely ignored by present speaker recognition research. However, these
trivial events are highly valuable in some particular circumstances such as
forensic examination, as they are less subjected to intentional change, so can
be used to discover the genuine speaker from disguised speech. In this paper,
we collect a trivial event speech database that involves 75 speakers and 6
types of events, and report preliminary speaker recognition results on this
database, by both human listeners and machines. Particularly, the deep feature
learning technique recently proposed by our group is utilized to analyze and
recognize the trivial events, which leads to acceptable equal error rates
(EERs) despite the extremely short durations (0.2-0.5 seconds) of these events.
Comparing different types of events, 'hmm' seems more speaker discriminative.
