  Informed speaker extraction aims to extract a target speech signal from a
mixture of sources given prior knowledge about the desired speaker. Recent deep
learning-based methods leverage a speaker discriminative model that maps a
reference snippet uttered by the target speaker into a single embedding vector
that encapsulates the characteristics of the target speaker. However, such
modeling deliberately neglects the time-varying properties of the reference
signal. In this work, we assume that a reference signal is available that is
temporally correlated with the target signal. To take this correlation into
account, we propose a time-varying source discriminative model that captures
the temporal dynamics of the reference signal. We also show that existing
methods and the proposed method can be generalized to non-speech sources as
well. Experimental results demonstrate that the proposed method significantly
improves the extraction performance when applied in an acoustic echo reduction
scenario.
