Speech driven talking head from estimated articulatory features

Abstract

In this paper, we present a talking head in which the lips and head motion are controlled using articulatory movements estimated from speech. A phone-size HMM-based inversion mapping is employed and trained in a semi-supervised fashion. The advantage of the use of articulatory features is that they can drive the lips motions and they have a close link with head movements. Speech inversion normally requires the training data recorded with electromagnetic articulograph (EMA), which restricts the naturalness of head movements. The present study considers a more realistic recording condition where the training data for the target speaker are recorded with a usual motion capture system rather than EMA. Different temporal clustering techniques are investigated for HMM-based mapping as well as a GMM-based frame-wise mapping as a baseline system. Objective and subjective experiments show that the synthesised motions are more natural using an HMM system than a GMM one, and estimated EMA features outperform prosodic features.