Sign Language Interpreter Training

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Sign Language Interpreter using Kinect Motion Sensor using Machine Learning

Sign Language Interpreter using Kinect Motion Sensor using Machine Learning

In 1991 Tomoichi Takahashi developed a system for real-time SLR that required from signer wearing of gloves. Gloves were connected with wires to the computer for transmission of hand configuration and joint angles [8]. HMMs can be used for online training mode. This was demonstrated in 1996 in a system that employed wired glovesfor feature extraction and HMM for gesture recognition [9]. CyberGlove with 18 sensors and connected to computer through serial cable was used for transmission of 20 hand joints [9]. The system recognized 14 letters from sign language alphabet while training of the system with one or two examples was enough for sign recognition [9]. A wireless glove that was designed by Ryan Petters in 2002, sensed hand movements involved in sign language and then transmitted them wirelessly to portable device, that displayed translated signs as lines of text [10].
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Indian Sign Language Interpreter with Android Implementation

Indian Sign Language Interpreter with Android Implementation

The Adaptive Boosting (Adaboost) learning algorithms can integrate the information of a category of objects. This algorithm originally used by Viola-Jones algorithm to train the sample set which involves cascade based classifier. It can combine the weak classifiers which cannot provide satisfactory result to become a strong classifier to get the better result. The Adaboost learning algorithm chooses the best weak classifier from a set of positive and negative images. After choosing the best weak classifier, the weights of the training images are adjusted by Adaboost algorithms. In this round the weights of classified training images are decreased and the unclassified images are increased. In the next round, the unclassified images will be more focused by the Adaboost and try to correctly classify the misclassified images. The whole procedures are finished until a predefined performance is satisfied. However, Ko-ChihWang (2007) [10] reported that the result of using AdaBoost with Viola Jones detector in hand detection is worse than face detection accuracy due to the structural problem of hand. And, he proposed using AdaBoost with SIFT Feature is more accurately. Therefore, it is necessary to apply AdaBoost
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Sign Language Recognition for Deaf Sign User

Sign Language Recognition for Deaf Sign User

Abstract— Sign language recognition is one of the most growing fields of research today and it is the most natural way of communication for the people with hearing problems. A hand gesture recognition system can provide an opportunity for deaf persons to communicate with normal people without the need of an interpreter or intermediate. We are going to build a systems and methods for the automatic recognition of Marathi sign language. Through that we are providing teaching classes for the purpose of training the deaf sign user in Marathi. The system does require hand to be properly aligned to the camera and does not need any special colour markers, glove or wearable sensors. A large set of samples has been used in proposed system to recognize isolated words from the standard Marathi sign language which are taken in front of camera by different deaf sign user. In our proposed system, we intend to recognize some very basic elements of sign language and to translate them to text and vice versa.
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Curriculum for Sign Language Interpreter

Curriculum for Sign Language Interpreter

This bachelor’s program is designed to work in collaboration with a number of community colleges and branch campuses that currently offer an Associates degree in Interpreting Training program (ITP). Wright State University (WSU) is in a unique position to provide leadership for this collaborative effort. Geographic location is excellent allowing WSU to serve as the regional hub for participating Associate degree granting institutions in Ohio’s south/central markets of Cincinnati, Chillicothe, Columbus, and Dayton.

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The Impact of American Sign Language Interpreter Licensure Laws on d/Deaf Defendants in Criminal Cases

The Impact of American Sign Language Interpreter Licensure Laws on d/Deaf Defendants in Criminal Cases

to best be used by each individual. Although this is something trained interpreters may learn to do naturally as part of their job, it is possible that some may not have been trained to do so. They may be interpreting for someone without awareness that the interpretation is not ideal, which could harm the individual. Although the licensure law and surrounding guidelines are written extremely well, addressing the vast majority of the issues presented in this study, that is only if the interpreter training is training more than just ASL and is also training in identifying the best form of communication for each individual. I believe that the ultimate goal should be to amend the ADA to provide a better framework for what a qualified interpreter means. I believe that research should be done to analyze the Maine law to determine where it may have additional holes to be filled and hope to apply the best possible version of this law into the ADA to protect d/Deaf Americans in every state.
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Gesture Acquisition and Recognition of Sign Language

Gesture Acquisition and Recognition of Sign Language

or official languages of human communication in some countries like the USA, Finland, the Czech Republic, France, the Russian Federation (since 2013) etc [2]. According to the statistics of medical organizations, about 0.1% of the population of any country is absolutely deaf and the most of such people communicate only by sign languages; many people, who were born deaf, even are not able to read. Additionally to conversational sign languages there are also fingerspelling alphabets, which are used to spell words (names, rare words, unknown signs, etc.) letter-by-letter. Developing algorithms and techniques to correctly recognize a sequence of produced signs and understand their meaning is called sign language recognition (SLR).SLR is a hybrid research area involving pattern recognition, natural language processing, computer vision and linguistics [3]. Sign Language recognition systems can be used as an interface between human being and computer systems. Sign languages are complete natural language with their phonology, morphology, syntax and grammar. A sign language is a visual-gesture language that is developed to facilitate the differently abled persons by creating visual gestures using face, hand, body and arms [4]. Sign language recognition is mainly consisting of three steps: preprocessing, feature extraction and classification. In preprocessing, a hand is detected from sign image or video. In feature extraction, various features are extracted from the image or video to produce the feature vector of the sign. Finally, in the classification, some samples of the images or videos are used for training the classifier then testing the signs in image or video.
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Building a Generator for Italian Sign Language

Building a Generator for Italian Sign Language

Natural language generation can be described as a three steps process: text planning, sentence planning and realization (Reiterand and Dale, 2000). Text planning determines which messages to communi- cate and how to rhetorically structure these mes- sages; sentence planning converts the text plan into a number of sentence plans; realization converts the sentence plans into the final sentences produced. Anyway, in the context of interlingua translation we simplify by assuming that generation needs only for the realization step. Our working hypothesis is that source and target sentences have as much as possi- ble the same text and the same sentence plans. This hypothesis is reasonable in our projects since we are working on a very peculiar sub-language (weather forecasts) where the rhetorical structure is usually very simple.
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Sign Language Glove using Arduino

Sign Language Glove using Arduino

Abstract: Communication between speakers and non-speakers of American Sign Language can be problematic, inconvenient, and expensive. This project attempts to bridge the communication gap by designing a portable glove that captures the user’s American Sign Language gestures and outputs the translated text on a laptop or a personal computer. The glove is equipped with flex sensors, contact sensors, and an accelerometer to measure the flexion of the fingers, the contact between fingers, and the rotation of the hand. The glove’s Arduino microcontroller analyses the sensor readings to identify the gesture. Using this device, one day speakers of American Sign Language may be able to communicate with others in an affordable and convenient way.
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Survey on Sign language to Speech Conversion

Survey on Sign language to Speech Conversion

ABSTRACT: Human being interact each other to convey their ideas ,thoughts , and experience to the people around them. But, there is some deaf mute people in the world. In this paper , the idea is proposed smart glove which can be convert sign language to speech output. The glove will help in producing artificial speech which produces daily communication for speech impaired person. Compared to other gestures like body, face ,and head ; hand gesture plays an important role, because it express as soon as reaction of users view. This paper shows flex sensor based gesture recognition module is develop to recognize English alphabet and few words and text to speech synthesizer. This is basically, data glove and microcontroller based system. Flex sensor based data glove can detect all the movement of the hand and microcontroller based system coverts some specified movement into human recognizablel voice. This paper provides map for developing such glove.
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Universal Dependencies for Swedish Sign Language

Universal Dependencies for Swedish Sign Language

Given that this is the first sign language UD tree- bank, we decided to perform some dependency parsing experiments to establish baseline results. We use the parser of Straka et al. (2015), part of the UDpipe toolkit (Straka et al., 2016), for our experiments. The training (334 tokens), develop- ment (48 tokens) and test (290 tokens) split from UD treebanks 1.4 was used. A hundred itera- tions of random hyperparameter search was per- formed for each of their parser models (projective, partially non-projective and fully non-projective), and the model with highest development set accu- racy was chosen. Unsurprisingly given the small amount of training data, we found the most con- strained projective model performed best, in spite of the data containing non-projective trees (see Figure 3). Development set attachment score was 60 and 56 (unlabeled and labeled, respectively) while the corresponding test set scores were 36 and 28. The discrepancy can be partly attributed to the much shorter mean sentence length of the development set: 6.0 vs 10.4 for the test set. Such low scores are not yet useful for practical tasks, but we emphasize that our primary goal in this work is to explore the possibility of UD annotation for a sign language. Our annotation project is ongoing, and we intend to further expand the SSL part in future UD treebanks releases.
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From Gesture to Sign Language: Conventionalization of Classifier Constructions by Adult Hearing Learners of British Sign Language

From Gesture to Sign Language: Conventionalization of Classifier Constructions by Adult Hearing Learners of British Sign Language

5 become fluent signers can reveal how gesture and signing coalesce as well as diverge. There is a sparse literature on adult learning of signed languages, and it focuses on iconicity at the lexical level (Campbell, Martin & White, 1992; Lieberth & Gamble, 1991; Ortega, 2012; Baus, Carreiras, & Emmorey, 2012). Lieberth & Gamble (1991) investigated non-signers’ recognition and retention of iconic and non-iconic (arbitrary) noun signs in American Sign Language (ASL), using a short- term and a long-term memory task. Both iconic and non-iconic signs were retained over a short and a long period of time, but there was a significant decrease in the number of non-iconic signs retained as the period of time after training increased, suggesting participants were more able to assimilate the iconic signs into existing semantic networks.
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Adapting the Assessing British Sign Language Development: Receptive Skills Test into American sign language

Adapting the Assessing British Sign Language Development: Receptive Skills Test into American sign language

The BSL RST is the first standardized test of any signed language in the world that has been normed on a population and tested for reliability (Johnson, 2004). For this reason, researchers from several difference countries have chosen to adapt it into other signed languages. The advantage of adapting an existing test rather than developing an original test is that important considerations and decisions have already been evaluated. For example, the BSL RST is based on what is known about signed language acquisition and highlights grammatical features identified in the research as important indicators of proficiency, such as verb morphology and use of space (Herman, Holmes, & Woll, 1998). Considering that many signed languages share these important grammatical features it is likely that test items will be relevant in signed languages other than BSL.
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Bridging the gap between sign language machine translation and sign language animation using sequence classification

Bridging the gap between sign language machine translation and sign language animation using sequence classification

The non-manual components in the DSGS side of our par- allel corpus serve various linguistic functions. For example, in our domain of train announcements, we have observed that furrowed eyebrows often occurred during signs with negative polarity, such as the sign BESCHRÄNKEN (‘LIMIT’). Raised eyebrows often occurred during signs that express a warning or emphasis, e.g., the signs VORSICHT (‘CAUTION’) or SO- FORT (‘IMMEDIATELY’). The syntactic functions mentioned in Section 1.2, topicalization and rhetorical question, also occur frequently in the corpus; a few instances of conditional expres- sions are also present. Many of these syntactic non-manuals relate to specific words in the sentence (e.g., rhetorical question non-manual components co-occur with question words, such as “WHAT”). Within this paper, we focus on such lexically-cued non-manuals. (As discussed in Section 4, we are aware that not all non-manual components are predictable based on the se- quence of lexical items in the sentence alone, and we propose to investigate such non-manuals in future work.)
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American sign language finger challenge

American sign language finger challenge

In every country, at least two cultures coexist: Hearing and Deaf. How the two cultural communities use these terms is political, individual and personal, centering around identity issues, therefore making it a bicultural issue. Identity depends on the severity of the hearing loss, age of onset, social interaction, medical intervention, and oral and/or Deaf language fluency. Persons born profoundly deaf, who had a Deaf secondary education, and prefer using ASL, will undoubtedly identify themselves as Deaf. People who grew up with normal hearing before becoming either somewhat deaf or profoundly deaf, need time to adapt and to explore the Deaf community. It will take time before they know how to label themselves with the culturally appropriate and meaningful terms from the Deaf community. Concurrently, a person born with deafness of any degree, who grew up surrounded by Hearing people, does not know any Deaf people or ASL, had a mainstream education and medical intervention such as hearing aids, a cochlear implant, or speech therapy, may identify themselves as deaf or HH. Subsequently, any deaf, deafened or HH person who is exposed to the Deaf community, who then feels a kinship to the Deaf culture and acquires some ASL fluency may change their identity to Deaf, while comfortably existing in both cultures. n
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Deaf Culture & Sign Language Recognition

Deaf Culture & Sign Language Recognition

Deaf people were once considered clinically deficient, and were subjected to procedures to “remove” deafness in order to become “normal”. The oral language became the de facto condition for social acceptance. However, the Deaf have the right to an identity, language and culture. They have the right to access the available human possibilities such as symbolic communication, social interaction, learning, etc. Sign Language, of visual-spatial manner, is the natural language of the Deaf, capable of providing complex linguistic functionalities[1].
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Sign Language Glove with Voice Synthesizer

Sign Language Glove with Voice Synthesizer

Sign language is a language which instead of acoustically conveyed sound patterns, uses manual communication and body language to convey meaning. This can involve simultaneously combining hand shapes, orientation and movement of the hands, arms or body, and facial expressions to fluidly express a speaker's thoughts. Wherever communities of deaf people exist, sign language will be useful. Sign language is also used by persons who can hear, but cannot physically speak. While they utilize space for grammar in a way that spoken languages do not. Sign languages exhibit the same linguistic properties and use the same language faculty as spoken languages do. Hundreds of sign languages are in use around the world and are at the cores of local deaf cultures. Some sign languages have obtained some form of legal recognition, while others have no status at all. Deaf and dumb people use sign language to communicate with themselves and with common people. It is very difficult for the common people to understand this language. Though they can show their message in writing, it is not conveyable to the illiterate people. Sign language translating equipments helps in conveying their message to the common people. It translates their message in sign form to the normal understandable text or voice form. All over the world there are many deaf and dumb people. They are all facing the problem of communication. Our project is one such effort to overcome this communication barrier by developing a glove which senses the hand movement of the sign language through sensors and translates it into text and voice output.
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Sign Language Recognition by Pattern Matching

Sign Language Recognition by Pattern Matching

A sign language is a language which uses manual communication and body language to convey meaning. Normally, there is no problem when two deaf persons communicate using their common sign language. The problem arises when a deaf person wants to communicate with a non-deaf person. Usually both will be dissatisfied in a very short time. Signing has always been part of human communications. For thousands of years, deaf people have created and used signs among themselves. These signs were the only form of communication available for many deaf people. Within the variety of cultures of deaf people all over the world, signing evolved to form complete languages. Sign language is a form of manual communication and is one of the most natural ways of communication for most people in deaf community. There has been many researchers who are surging interest in recognizing human hand gestures.
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A Review on Indian Sign Language Recognition

A Review on Indian Sign Language Recognition

P.V.V.Kishore and P.Rajesh Kumar [16] again proposed a real time approach to recognize gestures of ISL. The input video to the sign language recognition system was made independent of the environment in which signer was present. Active contours were used to segment and track the non-rigid hands and head of the signer. The energy minimization of active contours was accomplished by using object color, texture, boundary edge map and prior shape information. A feature matrix was designed from segmented and tracked hand and head portions. This feature matrix dimensions were minimized by temporal pooling creating a row vector for each gesture video. Pattern classification of gestures was achieved by implementing fuzzy inference system. The proposed system could translate video signs into text and voice commands. Their data base had 351 gestures with each gesture repeated 10 times by 10 different users. A recognition rate of 96% for gestures in all background environments was achieved.
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A Review on Indian Sign Language Recognition

A Review on Indian Sign Language Recognition

IJSRR, 8(2) April. – June., 2019 Page 3154 The identical authors projected a whole skeleton of isolated Video primarily based Indian signing Recognition System (INSLR) 17 that integrates varied image process techniques and process intelligence techniques so as to cater to sentence recognition.. A wave primarily based video segmentation technique was projected that detects shapes of assorted hand signs and head movement in video based setup. form options of hand gestures were extracted mistreatment elliptical Fourier descriptions that to the best degree reduces the feature vectors for a picture. PCA was accustomed minimize the feature vector once more for a selected gesture video and therefore the options weren't suffering from scaling or rotation of gestures among a video. options generated mistreatment these techniques created the feature vector distinctive for a selected gesture. Recognition of gestures from the extracted options was done employing a kind fuzzy abstract thought system that used linear output membership functions. Finally the INSLR system utilized Associate in Nursing electronic equipment to play the recognized gestures together with text output. The system was tested employing a knowledge set of eighty words and sentences by ten totally different signers. Their system had a recognition rate of 96%. the identical authors summarize varied algorithms accustomed style an indication language recognition system 18 . They designed a true time signing recognition system that would acknowledge gestures of ISL from videos underneath totally different advanced backgrounds. they need done lots of works within the field of ISL recognition. they'd used fuzzy classification and Artificial Neural network classification. Segmenting and trailing of non-rigid hands and head of the signer in signing videos was achieved by mistreatment active contour models. Active contour energy diminution was done mistreatment signers hand and head color, texture, boundary and form data. Classification of signs was done by a synthetic neural network mistreatment error back propagation rule.
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SIGN LANGUAGE RECOGNITION ? A NEW TREND

SIGN LANGUAGE RECOGNITION ? A NEW TREND

Abstract : In the world of sign language, and gestures, a lot of research work has been done over the past three decades. This has brought about a gradual transition from isolated to continuous, and static to dynamic gesture recognition for operations on a limited vocabulary. In present scenario, human machine interactive systems facilitate communication between the deaf, and hearing people in real world situations. In order to improve the accuracy of recognition, many researchers have deployed methods such as HMM, Artificial Neural Networks, and Kinect platform. Effective algorithms for segmentation, classification, pattern matching and recognition have evolved. The main purpose of this paper is to analyze these methods and to effectively compare them, which will enable the reader to reach an optimal solution. This creates both, challenges and opportunities for sign language recognition related research.
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