Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
AC: An Audio Classifier to Classify Violent Extensive Audios Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Toward Machine Translation Linguistic Issues of Indian Sign Language

verfasst von : Vivek Kumar Verma, Sumit Srivastava

Erschienen in: Speech and Language Processing for Human-Machine Communications

Verlag: Springer Singapore

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Sign language is a gesture-based language for communication of deaf people. It is basically a nonverbal symbolic language which is usually used to speak with and by deaf people. Indian sign language is a linguistically under-investigated language. Research on Indian sign language linguistics is also limited because of unavailability of standard sign dictionary and the unavailability of such tools which provide any education for Indian sign language. In the interpretation between sign language and verbal spoken language, there is an intermediate step in which the sign language needs to be represented by some written notation. However, there is as yet no standard notation for Indian sign language. In this paper, we investigate intermediate notation form for sign language as suitable for the machine translation. We also discuss the challenges of other linguistic issues for machine translation on Indian sign language.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Adaptive Infrared Images Enhancement Using Fuzzy-Based Concepts
Nächstes Kapitel Analysis of Emotion Recognition System for Telugu Using Prosodic and Formant Features
Literatur
1.
Verma, V.K., Srivastava, S., Kumar, N.: A comprehensive review on automation of Indian sign language. In: 2015 International Conference on Advances in Computer Engineering and Applications (ICACEA). IEEE, New York (2015)
2.
Kishore, P.V.V., Rajesh Kumar, P.: A video based Indian sign language recognition system (INSLR) using wavelet transform and fuzzy logic. IACSIT Int. J. Eng. Technol. 4(5) (2012)
3.
Cooper, H., Holt, B., Bowden, R.: Sign language recognition. In: Visual Analysis of Humans. Springer, London (2011)
4.
5.
Dzikovska, M., et al.: BEETLE II: deep natural language understanding and automatic feedback generation for intelligent tutoring in basic electricity and electronics. Int. J. Artif. Intell. Edu. 24(3), 284–332 (2014)
6.
Stein, D., Schmidt, C., Ney, H.: Analysis, preparation, and optimization of statistical sign language machine translation. Mach. Transl. 26(4), 325–357 (2012)CrossRef
7.
Curiel, A., Collet, C.: Sign language lexical recognition with propositional dynamic logic. arXiv preprint arXiv: PP.1403.6636 (2014)
8.
Sako, S., Kitamura, T.: Subunit modeling for Japanese sign language recognition based on phonetically depend multi-stream hidden markov models. In: Universal Access in Human-Computer Interaction. Design Methods, Tools, and Interaction Techniques for Einclusion, pp. 548–555. Springer, Berlin (2013)
9.
Caridakis, G., Asteriadis, S., Karpouzis, K.: Non-manual cues in automatic sign language recognition. Pers. Ubiquit. Comput. 18(1), 37–46 (2014)CrossRef
10.
Borghi, A.M., et al.: The body and the fading away of abstract concepts and words: a sign language analysis. Front. Psychol. 5 (2014)
11.
Friginal, E., et al.: Linguistic characteristics of AAC discourse in the workplace. Discourse Stud. (2013)
12.
Chiu, M.M.: Statistical discourse analysis of an online discussion: cognition and social metacognition. In: Productive Multivocality in the Analysis of Group Interactions, pp. 417–433. Springer US (2013)
13.
El Kholy, A., Habash, N.: Orthographic and morphological processing for English–Arabic statistical machine translation. Mach. Transl. 26(1–2), 25–45 (2012
14.
15.
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv: 1406.1078 (2014)
16.
Sall, S.B., Sharma, R., Shedamkar, R.R.: Example based machine translation using natural language processing. Int. J. Sci. Eng. Res. 4(8), 1771–1776 (2013)
17.
Dong, T., Cremers, A.B.: A novel machine translation method for learning Chinese as a foreign language. In: Computational Linguistics and Intelligent Text Processing, pp. 343–354. Springer, Berlin (2014)
18.
Zafrulla, Z., et al.: American sign language recognition with the kinect. Proceedings of the 13th International Conference on Multimodal Interfaces. ACM, New York (2011)
19.
Guimarães, C., et al.: Deaf Culture and sign language writing system—a database for a new approach to writing system recognition technology. In: 2014 47th Hawaii International Conference on System Sciences (HICSS). IEEE, New York (2014)
20.
Hutchinson, J.: Literature Review: Analysis of Sign Language Notations for Parsing in Machine Translation of SASL (2012)
21.
Kaur, R., Kumar, P.: HamNoSys generation system for sign language. In: 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, New York (2014)
22.
Porta, J., et al.: A rule-based translation from written Spanish to Spanish Sign Language glosses. Comput. Speech Lang .28(3), 788–811 (2014)
23.
Bouzid, Y., et al.: Towards a 3d Signing Avatar from Signwriting Notation. Springer, Berlin (2012)
24.
Bouzid, Y., Jemni, M.: A virtual signer to interpret signwriting. In: Computers Helping People with Special Needs, pp. 458–465. Springer International Publishing (2014)
Metadaten
Titel
Toward Machine Translation Linguistic Issues of Indian Sign Language
verfasst von
Vivek Kumar Verma
Sumit Srivastava
Copyright-Jahr
2018
Verlag
Springer Singapore
Buch
Speech and Language Processing for Human-Machine Communications

Print ISBN: 978-981-10-6625-2

Electronic ISBN: 978-981-10-6626-9

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-981-10-6626-9_14

Neuer Inhalt

    Bildnachweise