Speaker-aware source separation methods are promising workarounds for major
difficulties such as arbitrary source permutation and unknown number of
sources. However, it remains challenging to achieve satisfying performance
provided a very short available target speaker utterance (anchor). Here we
present a novel "deep extractor network" which creates an extractor point for
the target speaker in a canonical high dimensional embedding space, and pulls
together the time-frequency bins corresponding to the target speaker. The
proposed model is different from prior works in that the canonical embedding
space encodes knowledges of both the anchor and the mixture during an
end-to-end training phase: First, embeddings for the anchor and mixture speech
are separately constructed in a primary embedding space, and then combined as
an input to feed-forward layers to transform to a canonical embedding space
which we discover more stable than the primary one. Experimental results show
that given a very short utterance, the proposed model can efficiently recover
high quality target speech from a mixture, which outperforms various baseline
models, with 5.2% and 6.6% relative improvements in SDR and PESQ respectively
compared with a baseline oracle deep attracor model. Meanwhile, we show it can
be generalized well to more than one interfering speaker.

该研究提出了一种新的基于深度提取器网络的说话人感知源分离方法，它能够在高维空间构建提取器点以提取目标说话人的信息。实验结果表明该方法可以高效地从混合音频中恢复高质量的目标语音，相比基线深度吸引子模型，SDR 和 PESQ 都有 5.2% 和 6.6% 的相对改进，并且该方法可以很好地推广到多个干扰说话人的情况下。