Title :
Acoustically-Driven Talking Face Synthesis using Dynamic Bayesian Networks
Author :
Xue, Jianxia ; Borgstrom, Jonas ; Jiang, Jintao ; Bernstein, Lynne E. ; Alwan, Abeer
Author_Institution :
California Univ., Los Angeles, CA
Abstract :
Dynamic Bayesian networks (DBNs) have been widely studied in multi-modal speech recognition applications. Here, we introduce DBNs into an acoustically-driven talking face synthesis system. Three prototypes of DBNs, namely independent, coupled, and product HMMs were studied. Results showed that the DBN methods were more effective in this study than a multilinear regression baseline. Coupled and product HMMs performed similarly better than independent HMMs in terms of motion trajectory accuracy. Audio and visual speech asynchronies were represented differently for coupled HMMs versus product HMMs
Keywords :
acoustics; audio-visual systems; belief networks; face recognition; hidden Markov models; speech processing; speech recognition; speech synthesis; visual perception; DBN; HMM; acoustically-driven talking face synthesis system; audio-visual speech; dynamic Bayesian network; hidden Markov model; multimodal speech recognition application; Bayesian methods; Context modeling; Feature extraction; Hidden Markov models; Network synthesis; Optical devices; Optical noise; Prototypes; Speech recognition; Speech synthesis;
Conference_Titel :
Multimedia and Expo, 2006 IEEE International Conference on
Conference_Location :
Toronto, Ont.
Print_ISBN :
1-4244-0366-7
Electronic_ISBN :
1-4244-0367-7
DOI :
10.1109/ICME.2006.262743
