A Linguistically Motivated Model for Speed and Pausing in Animations of American Sign Language

Subscribe (Full Service)


	Search: The ACM Digital Library The Guide

	Feedback

A Linguistically Motivated Model for Speed and Pausing in Animations of American Sign Language

Full text

Pdf (2.13 MB)

Source

ACM Transactions on Accessible Computing (TACCESS) archive
Volume 2 , Issue 2 (June 2009) table of contents

Article No. 9

Year of Publication: 2009

ISSN:1936-7228

Author

Matt Huenerfauth

The City University of New York, Queens College

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 20, Downloads (12 Months): 107, Citation Count: 0

Additional Information:

abstract references index terms collaborative colleagues

Tools and Actions:	Request Permissions Review this Article Save this Article to a Binder Display Formats: BibTeX EndNote ACM Ref

DOI Bookmark:	Use this link to bookmark this Article: http://doi.acm.org/10.1145/1530064.1530067 What is a DOI?

ABSTRACT

Many deaf adults in the United States have difficulty reading written English text; computer animations of American Sign Language (ASL) can improve these individuals’ access to information, communication, and services. Planning and scripting the movements of a virtual character’s arms and body to perform a grammatically correct and understandable ASL sentence is a difficult task, and the timing subtleties of the animation can be particularly challenging. After examining the psycholinguistics literature on the speed and timing of ASL, we have designed software to calculate realistic timing of the movements in ASL animations. We have built algorithms to calculate the time-duration of signs and the location/length of pauses during an ASL animation. To determine whether our software can improve the quality of ASL animations, we conducted a study in which native ASL signers evaluated the ASL animations processed by our algorithms. We have found that: (1) adding linguistically motivated pauses and variations in sign-durations improved signers’ performance on a comprehension task and (2) these animations were rated as more understandable by ASL signers.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Bellugi, U. and Fischer, S. 1972. A comparison of sign language and spoken language. Cognition 1, 2--3, 173--200.
	2	Yu-Hsien Chiu , Chung-Hsien Wu , Hung-Yu Su , Chih-Jen Cheng, Joint Optimization of Word Alignment and Epenthesis Generation for Chinese to Taiwanese Sign Synthesis, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.29 n.1, p.28-39, January 2007 [doi>10.1109/TPAMI.2007.15]
	3	R. Elliott , J. R. W. Glauert , J. R. Kennaway , I. Marshall , E. Safar, Linguistic modelling and language-processing technologies for Avatar-based sign language presentation, Universal Access in the Information Society, v.6 n.4, p.375-391, January 2008 [doi>10.1007/s10209-007-0102-z]
	4	Fischer, S., Delhourne, L., and Reed, C. 1999. Effects of rate of presentation on the reception of American Sign Language. J. Speech Lang. Hear. Resear. 42, 568--582.
	5	Stavroula-Evita Fotinea , Eleni Efthimiou , George Caridakis , Kostas Karpouzis, A knowledge-based sign synthesis architecture, Universal Access in the Information Society, v.6 n.4, p.405-418, January 2008 [doi>10.1007/s10209-007-0094-8]
	6	Grosjean, F. 1979. A study of timing in a manual and a spoken language: American sign language and English. J. Psycholinguist. Res. 8, 4, 379--405.
	7	Grosjean, F., Grosjean, L., and Lane, H. 1979. The patterns of silence: Performance structures in sentence production. Cogn. Psychol. 11, 58--81.
	8	Grosjean, F., Lane, H., Teuber, H., and Battison, R. 1981. The invariance of sentence performance structures across language modality. J. Exper. Psych. Hum. Percep. Perform. 7, 216--230.
	9	Heiman, G. and Tweney, R. 1981. Intelligibility and comprehension of time compressed sign language narratives. J. Psycholinguist. Res. 10, 1, 3--16.
	10	Holt, J. A. 1993. Stanford Achievement Test---8th Edition: Reading comprehension subgroup results. Amer. Ann. Deaf. 138, 172--175.
	11	Matt Huenerfauth , Mitch Marcus , Martha Palmer, Generating american sign language classifier predicates for english-to-asl machine translation, University of Pennsylvania, Philadelphia, PA, 2006
	12	Matt Huenerfauth, Generating American Sign Language animation: overcoming misconceptions and technical challenges, Universal Access in the Information Society, v.6 n.4, p.419-434, January 2008 [doi>10.1007/s10209-007-0095-7]
	13	Matt Huenerfauth, Evaluation of a psycholinguistically motivated timing model for animations of american sign language, Proceedings of the 10th international ACM SIGACCESS conference on Computers and accessibility, October 13-15, 2008, Halifax, Nova Scotia, Canada [doi>10.1145/1414471.1414496]
	14	Huenerfauth, M. 2008c. A linguistically motivated model for speed and pausing in animations of American Sign Language. Linguistic and Assistive Technologies Laboratory website. http://latlab.cs.qc.cuny.edu/taccess2009/. (Accessed 11/30/08).
	15	Matt Huenerfauth , Liming Zhao , Erdan Gu , Jan Allbeck, Evaluation of American Sign Language Generation by Native ASL Signers, ACM Transactions on Accessible Computing (TACCESS), v.1 n.1, p.1-27, May 2008 [doi>10.1145/1361203.1361206]
	16	K. Karpouzis , G. Caridakis , S. -E. Fotinea , E. Efthimiou, Educational resources and implementation of a Greek sign language synthesis architecture, Computers & Education, v.49 n.1, p.54-74, August, 2007 [doi>10.1016/j.compedu.2005.06.004]
	17	J. R. Kennaway , J. R. W. Glauert , I. Zwitserlood, Providing signed content on the Internet by synthesized animation, ACM Transactions on Computer-Human Interaction (TOCHI), v.14 n.3, p.15-es, September 2007 [doi>10.1145/1279700.1279705]
	18	Liddell, S. 2003. Grammar, Gesture, and Meaning in American Sign Language. Cambridge University Press, Cambridge, UK.
	19	Marshall, I. and Sáfár, E. 2005. Grammar development for sign language avatar-based synthesis. In Proceedings of the 11th International Conference on Human-Computer Interaction, C. Stephanidis Ed. Lawrence Erlbaum Associates, Mahwah, NJ.
	20	Mitchell, R., Young, T., Bachleda, B., and Karchmer, M. 2006. How many people use ASL in the United States? Why estimates need updating. Sign Lang. Stud. 6, 3, 306--335.
	21	Morrissey, S. and Way, A. 2005. An example-based approach to translating sign language. In Proceedings of the Workshop on Example-Based Machine Translation, 109--116.
	22	Neidle, C., Kegl, J., Maclaughlin, D., Bahan, B., and Lee, R. G. 2000. The Syntax of American Sign Language: Functional Categories and Hierarchical Structure. The MIT Press, Cambridge, MA.
	23	Sandler, W. and Lillo-Martin, D. 2006. Sign Language and Linguistic Universals. Cambridge University Press, Cambridge, UK.
	24	Sheard, M., Van Der Schoot, S., Zwitserlood, I., Verlinden, M., and Weber, I. 2004. Evaluation reports 1 & 2, European Union project Essential Sign Language Information on Government Networks.
	25	Shionome, T., Kamata, K., Yamamoto, H., and Fischer, S. 2005. Effects of display size on perception of Japanese sign language---Mobile access in signed language. In Proceedings of the 11th International Conference on Human-Computer Interaction, C. Stephanidis Ed. Lawrence Erlbaum Associates, Mahwah, NJ.
	26	Stein, D., Bungeroth, J., and Ney, H. 2006. Morpho-syntax based statistical methods for sign language translation. In Proceedings of the Conference of the European Association for Machine Translation, 169--177.
	27	Tartter, V. and Fischer, S. 1983. Perceptual confusion in ASL under normal and reduced (point-light display) conditions. In J. Kyle & B. Woll Eds., Language in Sign: An International Perspective on Sign Language, Croom Helm, London, 215--224.
	28	Lynette van Zijl , Dean Barker, South African Sign Language Machine Translation System, Proceedings of the 2nd international conference on Computer graphics, virtual Reality, visualisation and interaction in Africa, February 03-05, 2003, Cape Town, South Africa [doi>10.1145/602330.602339]
	29	Vink, M. and Schermer, T. 2005. Report of research on use of an avatar compared with drawings or films of gestures. Tech. rep., Dutch Sign Language Center.
	30	Wingfield, A., Tun, P., Koh, C., and Rosen, M. 1999. Regaining lost time: Adult aging and the effect of time restoration on recall of time-compressed speed. Psych. Aging, 14, 3, 380--389.