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

Tornay, Sandrine, Razavi, Marzieh, Camgöz, Necati Cihan, Bowden, Richard and Magimai.-Doss, Mathew (2019) HMM-based Approaches to Model Multichannel Information in Sign Language Inspired from Articulatory Features-based Speech Processing In: 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2019), 12-17 May 2019, Brighton, UK.

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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.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Tornay, Sandrine
Razavi, Marzieh
Camgöz, Necati Cihann.camgoz@surrey.ac.uk
Bowden, RichardR.Bowden@surrey.ac.uk
Magimai.-Doss, Mathew
Date : 12 May 2019
DOI : 10.1109/ICASSP.2019.8683167
Copyright Disclaimer : © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords : Sign language; Subunits; Articulatory Features; Hidden Markov Model
Related URLs :
Depositing User : Clive Harris
Date Deposited : 07 May 2019 11:50
Last Modified : 07 May 2019 11:55
URI: http://epubs.surrey.ac.uk/id/eprint/851751

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