Multimedia Mapping using Continuous State Space Models
In: MMSP, 29 Sep - 1 Oct, Siena, Italy.
In this paper a system that transforms speech waveforms to
animated faces are proposed. The system relies on a state space model to perform the mapping.
To create a photo realistic image an Active Appearance Model is used. The main contribution of the paper is to compare a Kalman filter and a Hidden Markov Model approach to the mapping.
It is shown that even though the HMM can get a higher test likelihood the Kalman filter is easier to train and the animation quality is better for the Kalman filter.