  Lipreading has a lot of potential applications such as in the domain of
surveillance and video conferencing. Despite this, most of the work in building
lipreading systems has been limited to classifying silent videos into classes
representing text phrases. However, there are multiple problems associated with
making lipreading a text-based classification task like its dependence on a
particular language and vocabulary mapping. Thus, in this paper we propose a
multi-view lipreading to audio system, namely Lipper, which models it as a
regression task. The model takes silent videos as input and produces speech as
the output. With multi-view silent videos, we observe an improvement over
single-view speech reconstruction results. We show this by presenting an
exhaustive set of experiments for speaker-dependent, out-of-vocabulary and
speaker-independent settings. Further, we compare the delay values of Lipper
with other speechreading systems in order to show the real-time nature of audio
produced. We also perform a user study for the audios produced in order to
understand the level of comprehensibility of audios produced using Lipper.
