  This paper describes a fast speaker search system to retrieve segments of the
same voice identity in the large-scale data. A recent study shows that Locality
Sensitive Hashing (LSH) enables quick retrieval of a relevant voice in the
large-scale data in conjunction with i-vector while maintaining accuracy. In
this paper, we proposed Random Speaker-variability Subspace (RSS) projection to
map a data into LSH based hash tables. We hypothesized that rather than
projecting on completely random subspace without considering data, projecting
on randomly generated speaker variability space would give more chance to put
the same speaker representation into the same hash bins, so we can use less
number of hash tables. Multiple RSS can be generated by randomly selecting a
subset of speakers from a large speaker cohort. From the experimental result,
the proposed approach shows 100 times and 7 times faster than the linear search
and LSH, respectively
