This review gives a general overview of techniques used in statistical parametric speech synthesis. One instance of these techniques, called hidden Markov model (HMM)-based speech synthesis, has recently been demonstrated to be very effective in synthesizing acceptable speech. This review also contrasts these techniques with the more conventional technique of unit-selection synthesis that has dominated speech synthesis over the last decade. The advantages and drawbacks of statistical parametric synthesis are highlighted and we identify where we expect key developments to appear in the immediate future.

Neurosurgical resection is a therapeutic intervention in the treatment of brain tumors. Precision of the resection can be improved by utilizing Magnetic Resonance Imaging (MRI) as an aid in decision making during Image Guided Neurosurgery (IGNS). Image registration adjusts pre-operative data according to intra-operative tissue deformation. Some of the approaches increase the registration accuracy by tracking image landmarks through the whole brain volume. High computational cost used to render these techniques inappropriate for clinical applications.In this paper we present a parallel implementation of a state of the art registration method, and a number of needed incremental improvements. Overall, we reduced the response time for registration of an average dataset from about an hour and for some cases more than an hour to less than seven minutes, which is within the time constraints imposed by neurosurgeons. For the first time in clinical practice we demonstrated, that with the help of distributed computing non-rigid MRI registration based on volume tracking can be computed intra-operatively.