32,433 research outputs found

    A survey on mouth modeling and analysis for Sign Language recognition

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    © 2015 IEEE.Around 70 million Deaf worldwide use Sign Languages (SLs) as their native languages. At the same time, they have limited reading/writing skills in the spoken language. This puts them at a severe disadvantage in many contexts, including education, work, usage of computers and the Internet. Automatic Sign Language Recognition (ASLR) can support the Deaf in many ways, e.g. by enabling the development of systems for Human-Computer Interaction in SL and translation between sign and spoken language. Research in ASLR usually revolves around automatic understanding of manual signs. Recently, ASLR research community has started to appreciate the importance of non-manuals, since they are related to the lexical meaning of a sign, the syntax and the prosody. Nonmanuals include body and head pose, movement of the eyebrows and the eyes, as well as blinks and squints. Arguably, the mouth is one of the most involved parts of the face in non-manuals. Mouth actions related to ASLR can be either mouthings, i.e. visual syllables with the mouth while signing, or non-verbal mouth gestures. Both are very important in ASLR. In this paper, we present the first survey on mouth non-manuals in ASLR. We start by showing why mouth motion is important in SL and the relevant techniques that exist within ASLR. Since limited research has been conducted regarding automatic analysis of mouth motion in the context of ALSR, we proceed by surveying relevant techniques from the areas of automatic mouth expression and visual speech recognition which can be applied to the task. Finally, we conclude by presenting the challenges and potentials of automatic analysis of mouth motion in the context of ASLR

    Co-thought and Co-speech Gestures Are Generated by the Same Action Generation Process

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    We thank Lucy Foulkes, Rachel Furness, Valentina Lee, and Zeshu Shao for their help with data collection; Paraskevi Argyriou for her help with reliability checks of gesture coding; and Agnieszka Konopka and Josje Praamstra for their help with proofreading this article.Peer reviewedPostprin

    NEW shared & interconnected ASL resources: SignStream® 3 Software; DAI 2 for web access to linguistically annotated video corpora; and a sign bank

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    2017 marked the release of a new version of SignStream® software, designed to facilitate linguistic analysis of ASL video. SignStream® provides an intuitive interface for labeling and time-aligning manual and non-manual components of the signing. Version 3 has many new features. For example, it enables representation of morpho-phonological information, including display of handshapes. An expanding ASL video corpus, annotated through use of SignStream®, is shared publicly on the Web. This corpus (video plus annotations) is Web-accessible—browsable, searchable, and downloadable—thanks to a new, improved version of our Data Access Interface: DAI 2. DAI 2 also offers Web access to a brand new Sign Bank, containing about 10,000 examples of about 3,000 distinct signs, as produced by up to 9 different ASL signers. This Sign Bank is also directly accessible from within SignStream®, thereby boosting the efficiency and consistency of annotation; new items can also be added to the Sign Bank. Soon to be integrated into SignStream® 3 and DAI 2 are visualizations of computer-generated analyses of the video: graphical display of eyebrow height, eye aperture, an

    THE CHILD AND THE WORLD: How Children acquire Language

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    HOW CHILDREN ACQUIRE LANGUAGE Over the last few decades research into child language acquisition has been revolutionized by the use of ingenious new techniques which allow one to investigate what in fact infants (that is children not yet able to speak) can perceive when exposed to a stream of speech sound, the discriminations they can make between different speech sounds, differentspeech sound sequences and different words. However on the central features of the mystery, the extraordinarily rapid acquisition of lexicon and complex syntactic structures, little solid progress has been made. The questions being researched are how infants acquire and produce the speech sounds (phonemes) of the community language; how infants find words in the stream of speech; and how they link words to perceived objects or action, that is, discover meanings. In a recent general review in Nature of children's language acquisition, Patricia Kuhl also asked why we do not learn new languages as easily at 50 as at 5 and why computers have not cracked the human linguistic code. The motor theory of language function and origin makes possible a plausible account of child language acquisition generally from which answers can be derived also to these further questions. Why computers so far have been unable to 'crack' the language problem becomes apparent in the light of the motor theory account: computers can have no natural relation between words and their meanings; they have no conceptual store to which the network of words is linked nor do they have the innate aspects of language functioning - represented by function words; computers have no direct links between speech sounds and movement patterns and they do not have the instantly integrated neural patterning underlying thought - they necessarily operate serially and hierarchically. Adults find the acquisition of a new language much more difficult than children do because they are already neurally committed to the link between the words of their first language and the elements in their conceptual store. A second language being acquired by an adult is in direct competition for neural space with the network structures established for the first language

    Detection of major ASL sign types in continuous signing for ASL recognition

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    In American Sign Language (ASL) as well as other signed languages, different classes of signs (e.g., lexical signs, fingerspelled signs, and classifier constructions) have different internal structural properties. Continuous sign recognition accuracy can be improved through use of distinct recognition strategies, as well as different training datasets, for each class of signs. For these strategies to be applied, continuous signing video needs to be segmented into parts corresponding to particular classes of signs. In this paper we present a multiple instance learning-based segmentation system that accurately labels 91.27% of the video frames of 500 continuous utterances (including 7 different subjects) from the publicly accessible NCSLGR corpus (Neidle and Vogler, 2012). The system uses novel feature descriptors derived from both motion and shape statistics of the regions of high local motion. The system does not require a hand tracker
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