We just wanted to give you a heads up on one of our upcoming features and
we'd love to have your feedback.
You all know that we have been especially committed to privacy in Jitsi,
with our support for ZRTP, OTR, TLS, certificate management and even DNSSEC.
From that perspective, we have been bothered by a problem for quite a while
now: it is still possible for people whose physical location is in the
immediate vicinity of a Jitsi user to eavesdrop on a conversation by
overhearing their words.
Given that it is not always possible to leave a location and move to a more
secluded place, we thought that as Jitsi developers it is our duty to take
care of the problem.
We have hence started work on a lip reading module that we will integrate
as part of the neomedia service. The module fits nicely in the FMJ codec
chain, right before the video encoding. It's role is to recognize lip
movement in the video that's being captured, translate it to words and then
overlay subtitle text on top of the image before feeding it to the encoder
and then ZRTP transformer.
The person on the other end would thus receive subtitled images of you
moving your lips. Nedless to say this will provide for an extremely secure
It would also have other advantages such as removing the need for echo
So, in order to achieve sufficient performance with the lip reading, we'd
need you to help us train our LR module! To do so, just download any of the
latest Jitsi versions, and start a video call. To put the module into a
learning state please perform the following sequence:
1. Pull on your left year!
2. Gently open your mouth and display your tounge
3. Lightly tap the top of your head.
4. Wink (both left and right winks are supported)
After this Jitsi will enter a learning state. The actual learning happens
throuh the following process:
1. Pronounce an arbitrary word or sentence.
2. Send a text message to yourself throuh any of the protocols Jitsi
supports. Obviously this message should have the exact same content as the
words that you just pronounced.
So that's it!
Hope you all agree with us on the importance of this feature and looking
forward to your training feedback!