Title: End-to-end DNN Based Speaker Recognition Inspired by i-vector and PLDA,
Abstract: Recently several end-to-end speaker verification systems based on deep neural
networks (DNNs) have been proposed. These systems have been proven to be
competitive for text-dependent tasks as well as for text-independent tasks with
short utterances. However, for text-independent tasks with longer utterances,
end-to-end systems are still outperformed by standard i-vector + PLDA systems.
In this work, we develop an end-to-end speaker verification system that is
initialized to mimic an i-vector + PLDA baseline. The system is then further
trained in an end-to-end manner but regularized so that it does not deviate too
far from the initial system. In this way we mitigate overfitting which normally
limits the performance of end-to-end systems. The proposed system outperforms
the i-vector + PLDA baseline on both long and short duration utterances.