  Humans tend to change their way of speaking when they are immersed in a noisy
environment, a reflex known as Lombard effect. Current speech enhancement
systems based on deep learning do not usually take into account this change in
the speaking style, because they are trained with neutral (non-Lombard) speech
utterances recorded under quiet conditions to which noise is artificially
added. In this paper, we investigate the effects that the Lombard reflex has on
the performance of audio-visual speech enhancement systems based on deep
learning. The results show that a gap in the performance of as much as
approximately 5 dB between the systems trained on neutral speech and the ones
trained on Lombard speech exists. This indicates the benefit of taking into
account the mismatch between neutral and Lombard speech in the design of
audio-visual speech enhancement systems.
