Multimedia Mapping using Continuous State Space Models
Tue Lehn-Schiøler
In: MMSP, 29 Sep - 1 Oct, Siena, Italy.

Abstract

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.

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