  We present the recent advances along with an error analysis of the IBM
speaker recognition system for conversational speech. Some of the key
advancements that contribute to our system include: a nearest-neighbor
discriminant analysis (NDA) approach (as opposed to LDA) for intersession
variability compensation in the i-vector space, the application of speaker and
channel-adapted features derived from an automatic speech recognition (ASR)
system for speaker recognition, and the use of a DNN acoustic model with a very
large number of output units (~10k senones) to compute the frame-level soft
alignments required in the i-vector estimation process. We evaluate these
techniques on the NIST 2010 SRE extended core conditions (C1-C9), as well as
the 10sec-10sec condition. To our knowledge, results achieved by our system
represent the best performances published to date on these conditions. For
example, on the extended tel-tel condition (C5) the system achieves an EER of
0.59%. To garner further understanding of the remaining errors (on C5), we
examine the recordings associated with the low scoring target trials, where
various issues are identified for the problematic recordings/trials.
Interestingly, it is observed that correcting the pathological recordings not
only improves the scores for the target trials but also for the nontarget
trials.
