  In this paper we describe the recent advancements made in the IBM i-vector
speaker recognition system for conversational speech. In particular, we
identify key techniques that contribute to significant improvements in
performance of our system, and quantify their contributions. The techniques
include: 1) a nearest-neighbor discriminant analysis (NDA) approach that is
formulated to alleviate some of the limitations associated with the
conventional linear discriminant analysis (LDA) that assumes Gaussian
class-conditional distributions, 2) the application of speaker- and
channel-adapted features, which are derived from an automatic speech
recognition (ASR) system, for speaker recognition, and 3) the use of a deep
neural network (DNN) acoustic model with a 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 speaker
recognition evaluation (SRE) extended core conditions involving telephone and
microphone trials. Experimental results indicate that: 1) the NDA is more
effective (up to 35% relative improvement in terms of EER) than the traditional
parametric LDA for speaker recognition, 2) when compared to raw acoustic
features (e.g., MFCCs), the ASR speaker-adapted features provide gains in
speaker recognition performance, and 3) increasing the number of output units
in the DNN acoustic model (i.e., increasing the senone set size from 2k to 10k)
provides consistent improvements in performance (for example from 37% to 57%
relative EER gains over our baseline GMM i-vector system). To our knowledge,
results reported in this paper represent the best performances published to
date on the NIST SRE 2010 extended core tasks.
