  In this paper, we present a novel system that separates the voice of a target
speaker from multi-speaker signals, by making use of a reference signal from
the target speaker. We achieve this by training two separate neural networks:
(1) A speaker recognition network that produces speaker-discriminative
embeddings; (2) A spectrogram masking network that takes both noisy spectrogram
and speaker embedding as input, and produces a mask. Our system significantly
reduces the speech recognition WER on multi-speaker signals, with minimal WER
degradation on single-speaker signals.
