Every year, some 17,500 people in Europe and North America lose the power of speech after undergoing a laryngectomy, normally as a treatment for throat cancer. Several research groups have recently demonstrated that it is possible to restore speech to these people by using machine learning to learn the transformation from articulator movement to sound. In our project articulator movement is captured by a technique developed by our collaborators at Hull University called Permanent Magnet Articulography (PMA), which senses the changes of magnetic field caused by movements of small magnets attached to the lips and tongue. This solution, however, requires synchronous PMA-and-audio recordings for learning the transformation and, hence, it cannot be applied to people who have already lost their voice. Here we propose to investigate a variant of this technique in which the PMA data are used to drive an articulatory synthesiser, which generates speech acoustics by simulating the airflow through a computational model of the vocal tract. The project goals, participants, current status, and achievements of the project are discussed below.
