  The objective of this paper is to separate a target speaker's speech from a
mixture of two speakers using a deep audio-visual speech separation network.
Unlike previous works that used lip movement on video clips or pre-enrolled
speaker information as an auxiliary conditional feature, we use a single face
image of the target speaker. In this task, the conditional feature is obtained
from facial appearance in cross-modal biometric task, where audio and visual
identity representations are shared in latent space. Learnt identities from
facial images enforce the network to isolate matched speakers and extract the
voices from mixed speech. It solves the permutation problem caused by swapped
channel outputs, frequently occurred in speech separation tasks. The proposed
method is far more practical than video-based speech separation since user
profile images are readily available on many platforms. Also, unlike
speaker-aware separation methods, it is applicable on separation with unseen
speakers who have never been enrolled before. We show strong qualitative and
quantitative results on challenging real-world examples.
