  Audio-visual speech enhancement (AV-SE) is the task of improving speech
quality and intelligibility in a noisy environment using audio and visual
information from a talker. Recently, deep learning techniques have been adopted
to solve the AV-SE task in a supervised manner. In this context, the choice of
the target, i.e. the quantity to be estimated, and the objective function,
which quantifies the quality of this estimate, to be used for training is
critical for the performance. This work is the first that presents an
experimental study of a range of different targets and objective functions used
to train a deep-learning-based AV-SE system. The results show that the
approaches that directly estimate a mask perform the best overall in terms of
estimated speech quality and intelligibility, although the model that directly
estimates the log magnitude spectrum performs as good in terms of estimated
speech quality.
