  Target speech extraction aims to extract, based on a given conditioning cue,
a target speech signal that is corrupted by interfering sources, such as noise
or competing speakers. Building upon the achievements of the state-of-the-art
(SOTA) time-frequency speaker separation model TF-GridNet, we propose
AV-GridNet, a visual-grounded variant that incorporates the face recording of a
target speaker as a conditioning factor during the extraction process.
Recognizing the inherent dissimilarities between speech and noise signals as
interfering sources, we also propose SAV-GridNet, a scenario-aware model that
identifies the type of interfering scenario first and then applies a dedicated
expert model trained specifically for that scenario. Our proposed model
achieves SOTA results on the second COG-MHEAR Audio-Visual Speech Enhancement
Challenge, outperforming other models by a significant margin, objectively and
in a listening test. We also perform an extensive analysis of the results under
the two scenarios.
