Name
Abstract | ||
|---|---|---|
Machine lipreading (MLR) is speech recognition from visual cues and a niche research problem in speech processing u0026 computer vision. Current challenges fall into two groups: the content of the video, such as rate of speech or; the parameters of the video recording e.g, video resolution. We show that HD video is not needed to successfully lipread with a computer. The term viseme is used in machine lipreading to represent a visual cue or gesture which corresponds to a subgroup of phonemes where the phonemes are visually indistinguishable. A phoneme is the smallest sound one can utter, because there are more phonemes per viseme, maps between units show a many-to-one relationship. Many maps have been presented, we compare these and our results show Leeu0027s is best. We propose a new method of speaker-dependent phoneme-to-viseme maps and compare these to Leeu0027s. Our results show the sensitivity of phoneme clustering and we use our new knowledge to augment a conventional MLR system. It has been observed in MLR, that classifiers need training on test subjects to achieve accuracy. Thus machine lipreading is highly speaker-dependent. Conversely speaker independence is robust classification of non-training speakers. We investigate the dependence of phoneme-to-viseme maps between speakers and show there is not a high variability of visemes, but there is high variability in trajectory between visemes of individual speakers with the same ground truth. This implies a dependency upon the number of visemes within each set for each individual. We show that prior phoneme-to-viseme maps rarely have enough visemes and the optimal size, which varies by speaker, ranges from 11-35. Finally we decode from visemes back to phonemes and into words. Our novel approach uses the optimum range visemes within hierarchical training of phoneme classifiers and demonstrates a significant increase in classification accuracy. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Computer Vision and Pattern Recognition | Sensory cue,Speech processing,Pattern recognition,Display resolution,Viseme,Gesture,Computer science,Speech recognition,Ground truth,Artificial intelligence,Decoding methods,Cluster analysis |
DocType | Volume | Citations |
Journal | abs/1710.01288 | 0 |
PageRank | References | Authors |
0.34 | 39 | 1 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Helen L. Bear | 1 | 30 | 7.10 |