Name
Abstract | ||
|---|---|---|
This paper presents an initial implementation and evaluation of a system that synthesizes visual speech directly from the acoustic waveform. An artifical neural network (ANN) was trained to map the cepstral coefficients of an individual's natural speech to the control parameters of an animated synthetic talking head. We trained on two data sets; one was a set of 400 words spoken in isolation by a single speaker and the other a subset of extemporaneous speech from 10 different speakers. The system showed learning in both cases. A perceptual evaluation test indicated that the system's generalization to new words by the same speaker provides significant visible information, but significantly below that given by a text-to-speech algorithm. |
Year | Venue | Keywords |
|---|---|---|
1999 | AVSP | speech synthesis,neural network,artificial neural network,text to speech |
Field | DocType | Citations |
Motion capture,Speech synthesis,Gesture,Computer science,Communication channel,Speech recognition,Coarticulation,Animation,Artificial neural network,Perception | Conference | 40 |
PageRank | References | Authors |
1.90 | 8 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dominic W. Massaro | 1 | 391 | 49.07 |
| Jonas Beskow | 2 | 668 | 96.64 |
| Michael M. Cohen | 3 | 268 | 34.79 |
| Christopher L. Fry | 4 | 40 | 2.23 |
| Tony Rodriguez | 5 | 41 | 2.29 |