Sign

Résumé

A sign is an object, quality, event, or entity whose presence or occurrence indicates the probable presence or occurrence of something else. A natural sign bears a causal relation to its object—for instance, thunder is a sign of storm, or medical symptoms a sign of disease. A conventional sign signifies by agreement, as a full stop signifies the end of a sentence; similarly the words and expressions of a language, as well as bodily gestures, can be regarded as signs, expressing particular meanings. The physical objects most commonly referred to as signs (notices, road signs, etc., collectively known as signage) generally inform or instruct using written text, symbols, pictures or a combination of these. The philosophical study of signs and symbols is called semiotics; this includes the study of semiosis, which is the way in which signs (in the semiotic sense) operate. Nature Semiotics, epistemology, logic, and philosophy of language are concerned about the nature of sign

Source officielle

https://en.wikipedia.org/wiki/Sign

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Explainable Phonology-based Approach for Sign Language Recognition and Assessment

Sandrine Tornay

Sign language technology, unlike spoken language technology, is an emerging area of research. Sign language technologies can help in bridging the gap between the Deaf community and the hearing community. One such computer-aided technology is sign language learning technology. To build such a technology, there is a need for sign language technologies that can assess sign production of learners in a linguistically valid manner. Such a technology is yet to emerge. This thesis is a step towards that, where we aim to develop an "explainable" sign language assessment framework. Development of such a framework has some fundamental open research questions: (a) how to effectively model hand movement channel? (b) how to model the multiple channels inherent in sign language? and (c) how to assess sign language at different linguistic levels? The present thesis addresses those open research questions by: (a) development of a hidden Markov model (HMM) based approach that, given only pairwise comparison between signs, derives hand movement subunits that are sharable across sign languages and domains; (b) development of phonology-based approaches, inspired from modeling of articulatory features in speech processing, to model the multichannel information inherent in sign languages in the framework of HMM, and validating it through monolingual, cross-lingual and multilingual sign language recognition studies; and (c) development of a phonology-based sign language assessment approach that can assess in an integrated manner a produced sign at two different levels, namely, lexeme level (i.e., whether the sign production is targeting the correct sign or not) and at form level (i.e. whether the handshape production and the hand movement production is correct or not), and validating it on the linguistically annotated Swiss German Sign Language database SMILE.

EPFL2021

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Sign changes of Kloosterman sums with almost prime moduli

Ping Xi

We prove that the Kloosterman sum changes sign infinitely often as runs over squarefree moduli with at most 10 prime factors, which improves the previous results of Fouvry and Michel, Sivak-Fischler and Matomaki, replacing 10 by 23, 18 and 15, respectively. The method combines the Selberg sieve, equidistribution of Kloosterman sums and spectral theory of automorphic forms.

Springer Verlag2015

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Sequence modeling for signs and gestures is an open research problem. In thatdirection, there is a sustained effort towards modeling signs and gestures as a se-quence of subunits. In this paper, we develop a novel approach to infer movementsubunits in a data-driven manner to model signs and gestures in the frameworkof hidden Markov models (HMM) given the skeleton information. This approachinvolves: (a) representation of position and movement information with measure-ment of hand positions relative to body parts (head, shoulders, hips); (b) modelingthese features to infer a sign-specific left-to-right HMM; and (c) clustering theHMM states to infer states or subunits that are shared across signs and updat-ing the HMM topology of signs. We investigate the application of the proposedapproach on sign and gesture recognition tasks, specifically on Turkish signs Hos-piSign database and Italian gestures Chalearn 2014 task. On both databases, ourstudies show that, while yielding competitive systems, the proposed approach leadsto a shared movement subunit representation that maintains discrimination acrosssigns and gestures.

Idiap2019

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