  Does speaking style variation affect humans' ability to distinguish
individuals from their voices? How do humans compare with automatic systems
designed to discriminate between voices? In this paper, we attempt to answer
these questions by comparing human and machine speaker discrimination
performance for read speech versus casual conversations. Thirty listeners were
asked to perform a same versus different speaker task. Their performance was
compared to a state-of-the-art x-vector/PLDA-based automatic speaker
verification system. Results showed that both humans and machines performed
better with style-matched stimuli, and human performance was better when
listeners were native speakers of American English. Native listeners performed
better than machines in the style-matched conditions (EERs of 6.96% versus
14.35% for read speech, and 15.12% versus 19.87%, for conversations), but for
style-mismatched conditions, there was no significant difference between native
listeners and machines. In all conditions, fusing human responses with machine
results showed improvements compared to each alone, suggesting that humans and
machines have different approaches to speaker discrimination tasks. Differences
in the approaches were further confirmed by examining results for individual
speakers which showed that the perception of distinct and confused speakers
differed between human listeners and machines.
