  In this paper we present a data-driven, integrated approach to speaker
verification, which maps a test utterance and a few reference utterances
directly to a single score for verification and jointly optimizes the system's
components using the same evaluation protocol and metric as at test time. Such
an approach will result in simple and efficient systems, requiring little
domain-specific knowledge and making few model assumptions. We implement the
idea by formulating the problem as a single neural network architecture,
including the estimation of a speaker model on only a few utterances, and
evaluate it on our internal "Ok Google" benchmark for text-dependent speaker
verification. The proposed approach appears to be very effective for big data
applications like ours that require highly accurate, easy-to-maintain systems
with a small footprint.
