  Speaker recognition is a task of identifying persons from their voices.
Recently, deep learning has dramatically revolutionized speaker recognition.
However, there is lack of comprehensive reviews on the exciting progress.
  In this paper, we review several major subtasks of speaker recognition,
including speaker verification, identification, diarization, and robust speaker
recognition, with a focus on deep-learning-based methods. Because the major
advantage of deep learning over conventional methods is its representation
ability, which is able to produce highly abstract embedding features from
utterances, we first pay close attention to deep-learning-based speaker feature
extraction, including the inputs, network structures, temporal pooling
strategies, and objective functions respectively, which are the fundamental
components of many speaker recognition subtasks. Then, we make an overview of
speaker diarization, with an emphasis of recent supervised, end-to-end, and
online diarization. Finally, we survey robust speaker recognition from the
perspectives of domain adaptation and speech enhancement, which are two major
approaches of dealing with domain mismatch and noise problems. Popular and
recently released corpora are listed at the end of the paper.
