  To extract the voice of a target speaker when mixed with a variety of other
sounds, such as white and ambient noises or the voices of interfering speakers,
we extend the Transformer network to attend the most relevant information with
respect to the target speaker given the characteristics of his or her voices as
a form of contextual information. The idea has a natural interpretation in
terms of the selective attention theory. Specifically, we propose two models to
incorporate the voice characteristics in Transformer based on different
insights of where the feature selection should take place. Both models yield
excellent performance, on par or better than published state-of-the-art models
on the speaker extraction task, including separating speech of novel speakers
not seen during training.
