HMM-based Approaches to Model Multichannel Information in Sign Language Inspired from Articulatory Features-based Speech Processing

Sandrine Tornay; Marzieh Razavi; Necati Cihan Camgöz; Richard Bowden; Mathew Magimai-Doss

ICASSP 2019accepted0 citations

HMM-based Approaches to Model Multichannel Information in Sign Language Inspired from Articulatory Features-based Speech Processing

Sandrine Tornay, Marzieh Razavi, Necati Cihan Camgöz, Richard Bowden, Mathew Magimai-Doss

Abstract

Sign language conveys information through multiple channels, such as hand shape, hand movement, and mouthing. Modeling this multichannel 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.

BibTeX

@inproceedings{icassp2019_hmmbasedapproach,
  title = {HMM-based Approaches to Model Multichannel Information in Sign Language Inspired from Articulatory Features-based Speech Processing},
  author = {Sandrine Tornay and Marzieh Razavi and Necati Cihan Camgöz and Richard Bowden and Mathew Magimai-Doss},
  booktitle = {ICASSP 2019},
  year = {2019}
}