Speech following sign language training in autistic children with minimal verbal language

1988 ◽  
Vol 18 (2) ◽  
pp. 217-229 ◽  
Author(s):  
Paul J. Yoder ◽  
Thomas L. Layton
Keyword(s):  
Sign Language ◽  
Language Training ◽  
Autistic Children ◽  
Verbal Language

Comparison of Sign versus Verbal Symbol Training in Retarded Adults

1982 ◽  
Vol 55 (2) ◽  
pp. 395-401 ◽  
Author(s):  
Kandace A. Penner ◽  
William N. Williams
Keyword(s):  
Sign Language ◽  
Representative Sample ◽  
Verbal Learning ◽  
Mentally Retarded ◽  
Language Training ◽  
Combined Approach ◽  
Verbal Language ◽  
Verbal Labels ◽  
Sign Training ◽  
The Relationship

Sign language as an alternative or as an augmentive system to verbal language training in the mentally retarded is in widespread use. This study began an exploration of the relationship between sign and verbal learning in 10 institutionalized severely mentally retarded adults. Three experimental groups were taught color labels. Three persons received sign training only, 4 more received verbal training only, and last 3 received combined verbal and sign training. Sign labels tended to be learned more efficiently than verbal labels by this small group. Combined sign and verbal training improved verbal learning whereas sign learning was not improved through the combined approach. Replication and extension of this preliminary work with a larger and more representative sample is needed.

A Comparison of Sign and Verbal Language Training with Nonverbal Retarded Children

1981 ◽  
Vol 24 (1) ◽  
pp. 113-119 ◽  
Author(s):  
James V. Kahn
Keyword(s):  
Placebo Group ◽  
Sign Language ◽  
Future Research ◽  
Language Training ◽  
Verbal Language ◽  
Language Groups ◽  
Significant Difference ◽  
Verbal Group ◽  
Group A ◽  
Speech Training

Twelve nonverbal, hearing, retarded children were matched and then randomly assigned to sign language training, speech training, and placebo groups. Four of the subjects received speech training using the Bricker, Dennison, and Bricker (1976) program, and four subjects received sign language training using an adaptation of the Bricker et al. program. The results indicated no statistically significant difference between the verbal and sign language groups though the sign group and verbal group did learn significantly more than the placebo group. A closer examination of the findings indicated that although all four of the sign language subjects learned some signs, only two of the four speech training subjects learned to say any words. The findings were interpreted as indicating that some nonverbal retarded children will benefit more from sign language than speech training. Suggestions for the direction of future research are given.

Comparison of American Sign Language Use Identification using Multi-Class SVM Classification, Backpropagation Neural Network, K - Nearest Neighbor and Naive Bayes

Teknik ◽  
2021 ◽  
Vol 42 (2) ◽  
pp. 137-148
Author(s):  
Vincentius Abdi Gunawan ◽  
Leonardus Sandy Ade Putra
Keyword(s):  
Neural Network ◽  
American Sign Language ◽  
Sign Language ◽  
Nearest Neighbor ◽  
American Sign ◽  
Classification Methods ◽  
K Nearest Neighbor ◽  
Backpropagation Neural Network ◽  
Svm Classification ◽  
Verbal Language

Communication is essential in conveying information from one individual to another. However, not all individuals in the world can communicate verbally. According to WHO, deafness is a hearing loss that affects 466 million people globally, and 34 million are children. So it is necessary to have a non-verbal language learning method for someone who has hearing problems. The purpose of this study is to build a system that can identify non-verbal language so that it can be easily understood in real-time. A high success rate in the system needs a proper method to be applied in the system, such as machine learning supported by wavelet feature extraction and different classification methods in image processing. Machine learning was applied in the system because of its ability to recognize and compare the classification results in four different methods. The four classifications used to compare the hand gesture recognition from American Sign Language are the Multi-Class SVM classification, Backpropagation Neural Network Backpropagation, K - Nearest Neighbor (K-NN), and Naïve Bayes. The simulation test of the four classification methods that have been carried out obtained success rates of 99.3%, 98.28%, 97.7%, and 95.98%, respectively. So it can be concluded that the classification method using the Multi-Class SVM has the highest success rate in the introduction of American Sign Language, which reaches 99.3%. The whole system is designed and tested using MATLAB as supporting software and data processing.

Non-verbal Language: Sign Language

2015 ◽  
Vol 4 (2) ◽  
pp. 15-20
Author(s):  
Дубинский ◽  
Vladimir Dubinskiy
Keyword(s):  
Sign Language ◽  
New Words ◽  
Verbal Language ◽  
Language System ◽  
Important State

Constructive signs that occupy the important state by forming and organizing of the think, the means of the speech property and subjective manner of the expression denotation in discourse are considered. The specifics of their content and constructive components and the role of the discoursemodal words in the utterance are determined$ their functional-stylistic attitude in the language system is fixed. The going linguistic changes are described; becoming obsolete and disappearing of some words, appearing of the new words and their structural-communicative state, the transformation of some language units meanings are described.

scholarly journals Acquisition of sign language by autistic children. I: Expressive labelling.

1978 ◽  
Vol 11 (4) ◽  
pp. 489-501 ◽  
Author(s):  
E G Carr ◽  
J A Binkoff ◽  
E Kologinsky ◽  
M Eddy
Keyword(s):  
Sign Language ◽  
Autistic Children

scholarly journals Adjusting the Margins: Harnessing the Foundations of American Sign Language for VR

Frameless ◽  
2019 ◽  
Vol 1 (1) ◽  
pp. 29-36
Author(s):  
Luane Davis Haggerty ◽  
Keyword(s):  
New York ◽  
American Sign Language ◽  
Sign Language ◽  
Performance Support ◽  
Movie Industry ◽  
American Sign ◽  
Verbal Language ◽  
Performance Technique ◽  
Movement Study ◽  
Historical Fact

Del-Sign is a physical approach to acting that uses elements of Francois Delsarte mime techniques with the foundations of American Sign Language. This acting and presentational technique uses cross-cultural physical communication as a way to deepen an actors’ performance, support a presenter’s lecture, or can be used as a format from which to create animations that communicate with or without verbal language. It is a historical fact that Deaf actors using the foundations of Sign Language influenced the movie industry (Higgins). In silent movie infancy Deaf performers were brought in as consultants to ensure that the gestures, relational positions, facial expression, camera angles and body language of the actors could have the strongest impact and the clearest meaning (Albert Ballin). At that time the standard acting technique was a codified movement study begun and refined by Francious Deslarte (1870-1890s Paris, 1880-1915 Steele MacKaye New York). By blending these two structures we find that an outline is gained for creating movement, posture and gesture (MPG) that easily communicates meaning. The applications of this performance technique are many and varied. From the obvious acting for stage application to lawyers, teachers, priests or other presenters. Del-Sign can now bridge into adding technology to the mix allowing for this approach to be used when creating characters and movement for VR, AR or MR.

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