Evidence map›Paper›PMID 24516528›Full record

ArticlePloS one2014

Discriminant features and temporal structure of nonmanuals in American Sign Language.

C Fabian Benitez-Quiroz, Kadir Gökgöz, Ronnie B Wilbur, Aleix M Martinez

Abstract read
In one paragraph

Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Labeled Graph Kernel for Behavior Analysis.IEEE transactions on pattern analysis and machine intelligence · 2016
    Article
  5. Article
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

C Fabian Benitez-QuirozThe Ohio State University, Columbus, Ohio, United States of America.
Kadir GökgözPurdue University, West Lafayette, Indiana, United States of America.
Ronnie B WilburPurdue University, West Lafayette, Indiana, United States of America.
Aleix M MartinezThe Ohio State University, Columbus, Ohio, United States of America.

Funding

A Study of the Computational Space of Facial Expressions of EmotionR01EY020834 · NEI · OHIO STATE UNIVERSITY · PI MARTINEZ, ALEIX M · 2010 to 2018
$2.1M
Computational Methods for Analysis of Mouth Shapes in Sign LanguagesR21DC011081 · NIDCD · OHIO STATE UNIVERSITY · PI MARTINEZ, ALEIX M · 2010 to 2011
$393k
NEI NIH HHS R01 EY 020834NEI NIH HHS R01 EY020834NIDCD NIH HHS R21 DC011081
6 · The paper itself

Abstract

To fully define the grammar of American Sign Language (ASL), a linguistic model of its nonmanuals needs to be constructed. While significant progress has been made to understand the features defining ASL manuals, after years of research, much still needs to be done to uncover the discriminant nonmanual components. The major barrier to achieving this goal is the difficulty in correlating facial features and linguistic features, especially since these correlations may be temporally defined. For example, a facial feature (e.g., head moves down) occurring at the end of the movement of another facial feature (e.g., brows moves up), may specify a Hypothetical conditional, but only if this time relationship is maintained. In other instances, the single occurrence of a movement (e.g., brows move up) can be indicative of the same grammatical construction. In the present paper, we introduce a linguistic-computational approach to efficiently carry out this analysis. First, a linguistic model of the face is used to manually annotate a very large set of 2,347 videos of ASL nonmanuals (including tens of thousands of frames). Second, a computational approach is used to determine which features of the linguistic model are more informative of the grammatical rules under study. We used the proposed approach to study five types of sentences--Hypothetical conditionals, Yes/no questions, Wh-questions, Wh-questions postposed, and Assertions--plus their polarities--positive and negative. Our results verify several components of the standard model of ASL nonmanuals and, most importantly, identify several previously unreported features and their temporal relationship. Notably, our results uncovered a complex interaction between head position and mouth shape. These findings define some temporal structures of ASL nonmanuals not previously detected by other approaches.

Indexed as

Manuals as TopicSign LanguageComputer SimulationDiscriminant AnalysisHumansSoftwareTime FactorsUnited StatesVideo Recording

Identifiers

PMID24516528
PMCPMC3916328

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.