Native American Signed Languages

2017 ◽  
Author(s):  
Jeffrey Davis
Keyword(s):  
Native American ◽  
Sign Language ◽  
Language Contact ◽  
The United States ◽  
Lexical Borrowing ◽  
Language Varieties ◽  
Endangered Language ◽  
Northern Cheyenne ◽  
Signed Language ◽  
Indian Sign Language

This chapter highlights the linguistic study of Native American signed language varieties, which are broadly referred to as American Indian Sign Language (AISL). It describes how indigenous sign language serves as an alternative to spoken language, how it is acquired as a first or second language, and how it is used both among deaf and hearing tribal members and internationally as a type of signed lingua franca. It discusses the first fieldwork carried out in over fifty years to focus on the linguistic status of AISL, which is considered an endangered language variety but is still used and learned natively by some members of various Indian nations across Canada and the United States (e.g. Assiniboine, Blackfeet/Blackfoot, Cherokee, Crow, Northern Cheyenne, Nakoda/Lakȟóta, and Mandan-Hidatsa). The chapter also addresses questions of language contact and spread, including code-switching and lexical borrowing, as well as historical linguistic questions.

Language Contact Considering Signed Language

2020 ◽  
pp. 678-693
Author(s):  
David Quinto-Pozos ◽  
Robert Adam
Keyword(s):  
American Sign Language ◽  
Sign Language ◽  
Language Contact ◽  
American English ◽  
American Sign ◽  
Signed Language ◽  
The World

Language contact of various kinds is the norm in Deaf communities throughout the world, and this allows for exploration of the role of the different kinds of modality (be it spoken, signed or written, or a combination of these) and the channel of communication in language contact. Drawing its evidence largely from instances of American Sign Language (ASL) this chapter addresses and illustrates several of these themes: sign-speech contact, sign-writing contact, and sign-sign contact, examining instances of borrowing and bilingualism between some of these modalities, and compares these to contact between hearing users of spoken languages, specifically in this case American English.

Language Ideologies, Policies, and Attitudes toward Signed Languages

2013 ◽  
Author(s):  
Joseph Hill
Keyword(s):  
United States ◽  
American Sign Language ◽  
Sign Language ◽  
Language Ideologies ◽  
Language Attitudes ◽  
The United States ◽  
Signed Languages ◽  
Signed Language ◽  
History Of ◽  
The Impact

This chapter describes how ideologies about signed languages have come about, and what policies and attitudes have resulted. Language ideologies have governed the formal recognition of signed language at local, national, and international levels, such as that of the United Nations. The chapter discusses three major areas in the study of attitudes toward signed languages: Attitudes versus structural reality; the social factors and educational policies that have contributed to language attitudes; and the impact of language attitudes on identity and educational policy. Even in the United States, American Sign Language does not get recognition as a language in every region, and the attempt to suppress sign language is still operative. This is a worldwide issue for many countries with histories of opposition tosigned languages that parallel the history of the United States.

Lexical acculturation, areal diffusion, lingua francas, and bilingualism

1996 ◽  
Vol 25 (2) ◽  
pp. 261-282
Author(s):  
Cecil H. Brown
Keyword(s):  
Native American ◽  
Pacific Northwest ◽  
Language Contact ◽  
Tierra Del Fuego ◽  
The Arctic ◽  
Native American Languages ◽  
Lexical Borrowing ◽  
Pacific Northwest Coast ◽  
Nation States ◽  
The Pacific

ABSTRACTThis study continues an investigation of lexical acculturation in Native American languages using a sample of 292 language cases distributed from the Arctic Circle to Tierra del Fuego (Brown 1994). Focus is on the areal diffusion of native language words for imported European Objects and concepts. Approximately 80% of all sharing of such terms is found to occur among closely genetically related languages. Amerindian languages only distantly related, or not related at all, tend to share native labels for acculturated items only when these have diffused to them from a lingua franca, such as Chinook Jargon (a pidgin trade language of the Pacific Northwest Coast) or Peruvian Quechua (the language of the Inca empire). Lingua francas also facilitate diffusion of terms through genetically related languages; but sometimes, as in the case of Algonquian languages, these are neither familiar American pidgins nor languages associated with influential nation states. An explanatory framework is constructed around the proposal that degree of bilingualism positively influences extent of lexical borrowing. (Amerindian languages, bilingualism, language contact, lexical acculturation, lexical diffusion, lingua francas)

Language Development in Deaf Children

2019 ◽  
pp. 338-368
Author(s):  
Aaron J. Newman
Keyword(s):  
Hearing Loss ◽  
Language Development ◽  
Sign Language ◽  
First Language ◽  
The United States ◽  
Assistive Technologies ◽  
Deaf Children ◽  
Future Research ◽  
Signed Language ◽  
Deaf Parents

Hearing loss affects over 1 billion people around the world and is the fifth leading cause of disability. In the United States, approximately 10,000 babies are born each year with significant hearing loss. Although assistive technologies such as cochlear implants (CIs) are available to restore hearing, deaf children who receive CIs on average show significantly poorer language skills and academic outcomes than their normally hearing peers. At the same time, a relatively small percentage of deaf children are born to deaf parents and learn sign language as their first language, and grow up to be excellent, fluent communicators who are bilingual in signed and spoken language. Historically, there has been significant tension between advocates of sign language and “oralists” who discouraged sign language use. This chapter provides a critical review of language development in deaf children, including those with CIs and those exposed to different kinds, and amounts, of signed language. The linguistic and educational outcomes of deaf children are considered in light of current understanding of neurodevelopment, sensitive periods, and neuroplasticity, while highlighting areas of controversy and important directions for future research. The chapter concludes with evidence-based recommendations in favor of sign language exposure for all deaf children.

INDIAN SIGN LANGUAGE RECOGNITION SYSTEM USING OPENPOSE

2019 ◽  
Vol 7 (2) ◽  
pp. 43
Author(s):  
MALHOTRA POOJA ◽  
K. MANIAR CHIRAG ◽  
V. SANKPAL NIKHIL ◽  
R. THAKKAR HARDIK ◽  
◽  
...  
Keyword(s):  
Sign Language ◽  
Recognition System ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Indian Sign Language

Indian Sign Language Recognition on PYNQ Board

2020 ◽  
Vol 13 ◽  
Author(s):  
Sukhendra Singh ◽  
G. N. Rathna ◽  
Vivek Singhal
Keyword(s):  
Sign Language ◽  
Hand Gesture ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Hand Gestures ◽  
Depth Images ◽  
Kinect Camera ◽  
Impaired People ◽  
Web Camera ◽  
Indian Sign Language

Introduction: Sign language is the only way to communicate for speech-impaired people. But this sign language is not known to normal people so this is the cause of barrier in communicating. This is the problem faced by speech impaired people. In this paper, we have presented our solution which captured hand gestures with Kinect camera and classified the hand gesture into its correct symbol. Method: We used Kinect camera not the ordinary web camera because the ordinary camera does not capture its 3d orientation or depth of an image from camera however Kinect camera can capture 3d image and this will make classification more accurate. Result: Kinect camera will produce a different image for hand gestures for ‘2’ and ‘V’ and similarly for ‘1’ and ‘I’ however, normal web camera will not be able to distinguish between these two. We used hand gesture for Indian sign language and our dataset had 46339, RGB images and 46339 depth images. 80% of the total images were used for training and the remaining 20% for testing. In total 36 hand gestures were considered to capture alphabets and alphabets from A-Z and 10 for numeric, 26 for digits from 0-9 were considered to capture alphabets and Keywords. Conclusion: Along with real-time implementation, we have also shown the comparison of the performance of the various machine learning models in which we have found out the accuracy of CNN on depth- images has given the most accurate performance than other models. All these resulted were obtained on PYNQ Z2 board.

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