Fingerspelling Alphabet Research Articles

Mediated and non-mediated tactile fingerspelling: a comparative study

ABSTRACT In the development of communication devices for individuals who are Deafblind, a significant challenge is achieving a seamless transition from human-generated to technology-mediated communication. This study compares the intelligibility of the Australian Deafblind tactile fingerspelling alphabet rendered on the HaptiComm tactile communication device with the same alphabet articulated by a human signer. After a short training period, participants identified the 26 English alphabet letters in both the mediated (device) and non-mediated (human) conditions. Results indicated that while participants easily identified most letters in the non-mediated condition, the mediated condition was more difficult to decipher. Specifically, letters presented on the palm or near the index finger had significantly lower recognition rates. These findings highlight the need for further research on the tactile features of communication devices and emphasize the importance of refining these features to enhance the reliability and readability of mediated tactile communication produced through tactile fingerspelling.

Getting Inspired: A Qualitative Study on the Use of the Inspirisles Role-Playing Game to Teach Middle Schoolers American Sign Language

Background The purpose of this study was to determine the effectiveness of a role-playing game (RPG) as a pedagogical tool for teaching beginning American Sign Language (ASL) to middle school students in a classroom setting. Methods In this qualitative study, n = 40 students participated in a 50-minute class centered on the ASL role-playing game, Inspirisles, for 45 days. To collect data on the student experience of the Inspirisles class, a post-process focus group was conducted with an external moderator and students (n =18) who chose to participate. Student responses were coded using applied thematic analysis. Results Students reported learning 50 to 100 words in American Sign Language, including numbers and the fingerspelling alphabet. Beyond language acquisition, they reported three main themes: a change in learning dispositions, positive social-emotional growth, and identification of important factors for success in continuing classes. Conclusion Students in the Inspirisles -based ASL class showed progress in learning ASL, with additional considerations for prosocial group learning in the classroom setting. However, further research is needed to see if this pedagogy is a viable option for long-term learning of ASL in the classroom setting.

HaptiComm: A Touch-Mediated Communication Device for Deafblind Individuals

Deafblindness is a unique disability characterized by a dual sensory reduction of both hearing and vision. For some Deafblind individuals, communication via touch may be their most accessible sensory channel. Multiple techniques that rely purely on touch exist within the Deafblind community. One method of interest referred to as “Deafblind Tactile Fingerspelling Alphabet” in Australia or “Deafblind Manual Alphabet” in the U.K., comprises twenty-six tactile symbols representing the letters of the Latin alphabet. This letter describes the HaptiComm, a device designed to reproduce the sensations generated during fingerspelling communication. The HaptiComm comprises an array of twenty-four strategically placed electrodynamic actuators specifically designed to produce distinct tactile sensations upon which the fingerspelling alphabet is constructed. The first experimental evaluation showed promising results suggesting further investigations related to the timing and the pace at which the stimuli are produced.

DEAF-BSL: Deep lEArning Framework for British Sign Language recognition

The recent development of disability studies in academic bodies has expedited the promotion of investigation on disability. With computer-aided tools, communication between the impaired person and someone who does not understand sign language could be accessible. A large number of people across the world are using sign language (e.g., British Sign Language (BSL) , Asian Sign Language (ASL) , Indian Sign Language (ISL) , etc.) with hand gestures for communication. In BSL recognition, the involvement of both hands overlapping each other becomes the main challenge. Moreover, BSL comprises ambiguous signs concerning viewpoint. However, existing traditional techniques seem in-stable, less accurate, and inefficient. In this work, the BSL fingerspelling alphabet recognition problem explores using a Deep learning framework to address the above-mentioned concerns. Convolutional Neural Network (CNN) is employed to detect and recognize for classification of 26 alphabets after being trained on the BSL corpus dataset. The proposed work outperforms the existing works with better precision (6%), recall (4%), and F-measure (5̃%). It reported better results on the BSL corpus dataset and webcam videos. The model achieved better accuracy (98.0%) for a large lexicon of words than previous models (Goh & Holden [ 6 ]: 69.5%, Rambhau [ 9 ]: 79.2%, and Liwicki et al. [ 8 ]: 92.5%). The 3D CNN-based proposal performs robust hand detection, much more accurate sign recognition, more scalability, and less ambiguity in BSL finger-spelling recognition.

Multivariate Scaling of the Characteristic Features Based on Pseudo-Inverse Operations for Recognition Problems Solving

Some approach to multidimensional information scaling of the characteristics features based on results of theory of perturbation of pseudo-inverse and projective matrices and solutions of systems of linear algebraic equations is proposed. The method and algorithm of a piecewise hyperplane clusters creating with the verification of a given criterion for effectiveness of the proposed method of clustering is developed. The problem of stability of main indicators of the classifier in presence disturbances in source information is investigated. The proposed method for determining influence of source data errors on main indicators of the classifier provides presentation of undisturbed information matrix in form of splitting matrices of special kind. Advantages of the proposed approach are demonstrated, an example of using the method of scaling characteristic features for recognizing fingerspelling alphabet of sign language is given.

Automatic recognition of the American sign language fingerspelling alphabet to assist people living with speech or hearing impairments

Sign languages are natural languages used mostly by deaf and hard of hearing people. Different development opportunities for people with these disabilities are limited because of communication problems. The advances in technology to recognize signs and gestures will make computer supported interpretation of sign languages possible. There are more than 137 different sign languages around the world; therefore, a system that interprets them could be beneficial to all, especially to the Deaf Community. This paper presents a system based on hand tracking devices (Leap Motion and Intel RealSense), used for signs recognition. The system uses a Support Vector Machine for sign classification. Different evaluations of the system were performed with over 50 individuals; and remarkable recognition accuracy was achieved with selected signs (100% accuracy was achieved recognizing some signs). Furthermore, an exploration on the Leap Motion and the Intel RealSense potential as a hand tracking devices for sign language recognition using the American Sign Language fingerspelling alphabet was performed.

Automatic Technologies for Processing Spoken Sign Languages

Sign languages are known as a natural means for verbal communication of the deaf and hard of hearing people. There is no universal sign language, and almost each country has its own national sign language and fingerspelling alphabet. Sign languages use visual-kinetic clues for human-to-human communication combining hand gestures with lips articulation and facial mimics. They also possess a special grammar that is quite different from that of speech-based spoken languages. Sign languages are spoken (silently) by a hundred million deaf people all over the world and the most popular are American (ASL), Chinese, Brazilian, Russian, and British Sign Languages; there are almost 140 such languages according to the Ethnologue. They do not have a natural written form, and there is a huge lack of electronic resources for them, in particular, vocabularies, audio-visual databases, automatic recognition and synthesis systems, etc. Thus, sign languages may be considered as non-written under-resourced spoken languages. In this paper, we present a computer system for text-to-sign language synthesis for the Russian and Czech Sign Languages.

Human Hand Motion Parametrization for Dactilemes Modeling

An approach to the study of human hand fingers motions to build specifications of fingerspelling units of a dactile sign language is considered. The information-parametric model of the man hand to describe the possible motions while the fingerspelling process of the elements of a sign language is proposed. The dactile alphabets of ten sign languages were studied, comparative analysis for their similarity and difference of motions construction was performed. An example of a training program for Ukrainian dactile fingerspelling alphabet, realized with the use of technology Net and Windows Presentation Foundation of Microsoft is given.

Management of tinnitus induced by brainstem and cerebellar infarction associated with complications of cerebello-pontine angle surgery

Following surgery in the USA in 1992 to remove a large right cerebello-pontine angle tumour, a 39-year-old woman developed severe brainstem and cerebellar infarction. This left her with severe visual impairment and ataxia. She became able to communicate by means of an adapted finger-spelling alphabet. She had total hearing loss in the right ear and a mild to moderately severe sensorineural hearing loss in the left ear, and severe tinnitus heard throughout the head. Additionally, she experienced hypersensitivity to sound above normal conversational levels, which evoked a synaesthetic feeling of coldness across her upper torso. Previous linear analogue hearing aid fitting had not been beneficial for either hearing or tinnitus. Careful fitting of a digital hearing aid, together with tinnitus counselling, inhibited the patient's tinnitus to 25 per cent of its former intensity after a six month acclimatisation period, and improved communication.

Comparing Sign Language and Speech Reveals a Universal Limit on Short-Term Memory Capacity

Short-term memory (STM) for signs in native signers consistently shows a smaller capacity than STM for words in native speakers (see Emmorey, 2002, for review). One explanation of this difference is based on the length effect: Short items yield higher spans than items that take longer to pronounce, presumably because of limited processing time. Signs in American Sign Language (ASL) take longer to articulate than English words (Bellugi & Fischer, 1972). This is not problematic in natural language use, because ASL conveys information simultaneously. However, with immediate serial recall, articulation time looms large. Some researchers have argued that articulation time is sufficient to account for the sign-speech difference in STM (Emmorey, 2002; Marschark & Mayer, 1998; Wilson, 2001; Wilson & Emmorey, 1997). If so, then STM capacity is, at its root, governed by a general processing limitation that is not affected by language modality. However, this claim has never been adequately tested. If articulation time does not fully account for the sign-speech difference in STM, then other differences between sign and speech may be important. In particular, because vision and audition have strikingly different information-processing characteristics, the sign-speech difference could be due to perceptually based coding. If so, then the principles governing STM are locally determined and cannot be generalized across language modalities. Recently Boutla, Supalla, Newport, and Bavelier (2004) addressed this question using stimuli that are articulated very rapidly in ASL. The digits 1 through 9 and the letters of the fingerspelling alphabet in ASL are produced with the fingers of one hand without large-scale movement, and therefore can be produced very quickly. However, the hand shapes for the digits 1 through 9 in ASL are similar, and formational similarity reduces STM (Klima & Bellugi, 1979; Wilson & Emmorey, 1997). Therefore, Boutla et al. used the Digit Span task from the Wechsler Adult Intelligence Scale (WAIS), but substituted ASL letters for digits. Signers were tested with ASL letters, and speakers with spoken English digits. Span was still longer for English than ASL. The authors concluded that STM for spoken language benefits from auditory-based representations and does not reflect a standard capacity of STM that applies across domains. However, that study compared signed letters with spoken digits, and recent evidence suggests that digits have a special status in STM, yielding better performance than otherwise matched lexical items (Jeffries, Patterson, Jones, Bateman, & Ralph, 2004). Thus, digits and letters may not be comparable categories for testing STM. A better option, then, would be to compare ASL letters with English letters. We report here the results of three experiments. The first two verified that digits yield better STM than letters. The third experiment returned to the original question: whether superiority of spoken language in STMpersists when articulatory duration is controlled. We used theWAIS Digit Span task (Wechsler, 1955), in which sequences of items are presented at a rate of one per second and must be repeated by the participant in the correct order. Sequences increase in length, with two sequences of each length, and the test concludes when the participant fails on both sequences of a particular length. One point is awarded for every correct sequence.