The Silent Speech Interface (SSI) can be defined as the technology which enables the
synthesis of speech in the absence of an audible acoustic signal. This technology can be
applied in many applications such as: providing a solution to laryngectomy patients, enabling
communication within noisy environments or via silent calls. This thesis addresses the
particular case of SSI using ultrasound images of the tongue as input signals.
In order to achieve our goal, we have chosen the Generative Adversarial Networks (GANs)
 as a branch of unsupervised learning techniques in machine learning which are able to
mimic any data distribution and generate data like it. And as a branch of GAN we have chosen
the conditional GAN in order to map our ultrasound images with the Mel generalized
coefficient required to synthesis the speech.
We have prepared our dataset that we are going to use for the training and the testing of
our network and explained the GAN architecture that we have adopted. Finally, we have
presented the objective and subjective evaluation of our approach.