Name
Title | ||
|---|---|---|
Large-Vocabulary Continuous Sign Language Recognition Based on Transition-Movement Models |
Abstract | ||
|---|---|---|
The major challenges that sign language recognition (SLR) now faces are developing methods that solve large-vocabulary continuous sign problems. In this paper, transition-movement models (TMMs) are proposed to handle transition parts between two adjacent signs in large-vocabulary continuous SLR. For tackling mass transition movements arisen from a large vocabulary size, a temporal clustering algorithm improved from k-means by using dynamic time warping as its distance measure is proposed to dynamically cluster them; then, an iterative segmentation algorithm for automatically segmenting transition parts from continuous sentences and training these TMMs through a bootstrap process is presented. The clustered TMMs due to their excellent generalization are very suitable for large-vocabulary continuous SLR. Lastly, TMMs together with sign models are viewed as candidates of the Viterbi search algorithm for recognizing continuous sign language. Experiments demonstrate that continuous SLR based on TMMs has good performance over a large vocabulary of 5113 Chinese signs and obtains an average accuracy of 91.9% |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/TSMCA.2006.886347 | IEEE Transactions on Systems, Man, and Cybernetics, Part A |
Keywords | Field | DocType |
hidden markov model (hmm),dynamic time warping (dtw),transition-movement models,pattern clustering,continuous sign language,adjacent sign,large-vocabulary continuous slr,continuous slr,temporal clustering algorithm,bootstrap process,chinese sign,large-vocabulary continuous sign language,large-vocabulary continuous sign problem,viterbi search algorithm,iterative segmentation algorithm,sign language recognition (slr),chinese sign language (csl),dynamic time warping,sign model,gesture recognition,continuous sentence,sign language recognition,transition part,chinese sign language,sign language,k means,indexing terms,hidden markov model,search algorithm | Chinese Sign Language,Dynamic time warping,Computer science,Gesture recognition,Artificial intelligence,Cluster analysis,Bootstrapping (electronics),Pattern recognition,Segmentation,Speech recognition,Sign language,Vocabulary,Machine learning | Journal |
Volume | Issue | ISSN |
37 | 1 | 1083-4427 |
Citations | PageRank | References |
48 | 1.93 | 21 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| G. Fang | 1 | 48 | 1.93 |
| Wen Gao | 2 | 11374 | 741.77 |
| Debin Zhao | 3 | 3010 | 206.12 |