Sciweavers

Share
ICMI
2010
Springer

Visual speech synthesis by modelling coarticulation dynamics using a non-parametric switching state-space model

9 years 3 months ago
Visual speech synthesis by modelling coarticulation dynamics using a non-parametric switching state-space model
We present a novel approach to speech-driven facial animation using a non-parametric switching state space model based on Gaussian processes. The model is an extension of the shared Gaussian process dynamical model, augmented with switching states. Audio and visual data from a talking head corpus are jointly modelled using the proposed method. The switching states are found using variable length Markov models trained on labelled phonetic data. We also propose a synthesis technique that takes into account both previous and future phonetic context, thus accounting for coarticulatory effects in speech. Categories and Subject Descriptors I.5.4 [Image Processing and Computer Vision]: Applications--Computer vision, Signal processing Keywords speech-driven facial animation, visual speech synthesis, artificial talking head General Terms algorithms, theory, experimentation
Salil Deena, Shaobo Hou, Aphrodite Galata
Added 04 Mar 2011
Updated 04 Mar 2011
Type Journal
Year 2010
Where ICMI
Authors Salil Deena, Shaobo Hou, Aphrodite Galata
Comments (0)
books