  This paper presents, a first of its kind, audio-visual (AV) speech enhacement
challenge in real-noisy settings. A detailed description of the AV challenge, a
novel real noisy AV corpus (ASPIRE), benchmark speech enhancement task, and
baseline performance results are outlined. The latter are based on training a
deep neural architecture on a synthetic mixture of Grid corpus and ChiME3
noises (consisting of bus, pedestrian, cafe, and street noises) and testing on
the ASPIRE corpus. Subjective evaluations of five different speech enhancement
algorithms (including SEAGN, spectrum subtraction (SS) , log-minimum
mean-square error (LMMSE), audio-only CochleaNet, and AV CochleaNet) are
presented as baseline results. The aim of the multi-modal challenge is to
provide a timely opportunity for comprehensive evaluation of novel AV speech
enhancement algorithms, using our new benchmark, real-noisy AV corpus and
specified performance metrics. This will promote AV speech processing research
globally, stimulate new ground-breaking multi-modal approaches, and attract
interest from companies, academics and researchers working in AV speech
technologies and applications. We encourage participants (through a challenge
website sign-up) from both the speech and hearing research communities, to
benefit from their complementary approaches to AV speech in noise processing.
