Name
Title | ||
|---|---|---|
Hmm-Based Approaches To Model Multichannel Information In Sign Language Inspired From Articulatory Features-Based Speech Processing |
Abstract | ||
|---|---|---|
Sign language conveys information through multiple channels, such as hand shape, hand movement, and mouthing. Modeling this multi-channel information is a highly challenging problem. In this paper, we elucidate the link between spoken language and sign language in terms of production phenomenon and perception phenomenon. Through this link we show that hidden Markov model-based approaches developed to model "articulatory" features for spoken language processing can be exploited to model the multichannel information inherent in sign language for sign language processing. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/icassp.2019.8683167 | 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) |
Keywords | Field | DocType |
Sign language, Subunits, Articulatory Features, Hidden Markov Model | Speech processing,Pattern recognition,Computer science,Communication channel,Speech recognition,Sign language,Artificial intelligence,Phenomenon,Mouthing,Hidden Markov model,Perception,Spoken language | Conference |
ISSN | Citations | PageRank |
1520-6149 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sandrine Tornay | 1 | 1 | 1.02 |
| Marzieh Razavi | 2 | 30 | 4.12 |
| Necati Cihan Camgöz | 3 | 39 | 9.23 |
| Richard Bowden | 4 | 1840 | 118.50 |
| Mathew Magimai-Doss | 5 | 516 | 54.76 |