Spoken language processing techniques for sign language recognition and translation.
We present an approach to automatically recognize sign language and translate it into a spoken language. A system to address these tasks is created based on state-of-the-art techniques from statistical machine translation, speech recognition, and image processing research. Such a system is necessary...
| Publicado en: | Technology & Disability Vol. 20; no. 2; pp. 121 - 134 |
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| Autores principales: | , , , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2008
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=105804799&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105804799 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10554181 3QS jtl: Technology & Disability issn: 10554181 maglogo: N pubinfo: dt: 2008 vid: 20 iid: 2 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 105804799 105804799 2009984123 10.3233/tad-2008-20207 105804799 ppf: 121 ppct: 13 formats: fmt: @attributes: type: P tig: atl: Spoken language processing techniques for sign language recognition and translation. aug: au: Dreuw P Stein D Deselaers T Rybach D Zahedi M Bungeroth J Ney H affil: Human Language Technology and Pattern Recognition, Computer Science Department, Aachen University, Germany sug: subj: Communication Computers and Computerization Deafness Sign Language Benchmarking Evaluation Research Models, Theoretical Speech Perception Systems Design Translations Human ab: We present an approach to automatically recognize sign language and translate it into a spoken language. A system to address these tasks is created based on state-of-the-art techniques from statistical machine translation, speech recognition, and image processing research. Such a system is necessary for communication between deaf and hearing people. The communication is otherwise nearly impossible due to missing sign language skills on the hearing side, and the low reading and writing skills on the deaf side. As opposed to most current approaches, which focus on the recognition of isolated signs only, we present a system that recognizes complete sentences in sign language. Similar to speech recognition, we have to deal with temporal sequences. Instead of the acoustic signal in speech recognition, we process a video signal as input. Therefore, we use a speech recognition system to obtain a textual representation of the signed sentences. This intermediate representation is then fed into a statistical machine translation system to create a translation into a spoken language. To achieve good results, some particularities of sign languages are considered in both systems. We use a publicly available corpus to show the performance of the proposed system and report very promising results. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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