CORPUS OF SIGNS IN WRITING AS A TOOL TO INVESTIGATE THE PECULIARITIES OF HOW SIGNS FORM UP (ON THE EXAMPLE OF THE RUSSIAN SIGN LANGUAGE)

We investigate the peculiarities of how gestures are formed in Sign Languages; deaf people use these gestures to communicate with each other. These peculiarities make it problematic to describe the Sign Languages linguistically. The Sign Language allows hear-impaired people to communicate non-verbally. It occupies a special place among all languages. The Sign Language is an independent full-fledged language with its own construction rules. In Sign Languages, almost every word conforms to a sign. Until recently, Sign Languages had no written form. The developed systems of gesture recording (notation) allow us to fix all their elements in the form of a sequence of signs. This makes it possible to transmit and store important information to use in various human activities. We describe the specifics of how sign (gesture) are formed in Russian Sign Language. We give examples of how gestures are recorded by means of the signs of the SignWriting system. At the moment, this system is the most common in many countries. One advantage of this notation is that the system describes gestures highly accurately. The second advantage is that the system is universally applicable to many national Sign Languages. This allows us to investigate the structure of gestures in a convenient static form, to reveal how the sign peculiarities form up. Our investigation is based on the elements of our own corpus (dictionary) of the most common gestures; the gestures are written in the SignWriting notation and grouped according to subject. The results we obtained can be useful for the researchers who specialize in Sign Languages and develop software to recognize Sign Languages. We analyzed the peculiarities of the Russian Sign Language by means of the SignWriting signs. We demonstrated how effective the written representation of gestures is to study Sign Languages

Rendering an avatar from sign writing notation for sign language animation

Magister Scientiae - MScThis thesis presents an approach for automatically generating signing animations from a sign language notation. An avatar endowed with expressive gestures, as subtle as changes in facial expression, is used to render the sign language animations. SWML, an XML format of SignWriting is provided as input. It transcribes sign language gestures in a format compatible to virtual signing. Relevant features of sign language gestures are extracted from the SWML. These features are then converted to body animation pa- rameters, which are used to animate the avatar. Using key-frame animation techniques, intermediate key-frames approximate the expected sign language gestures. The avatar then renders the corresponding sign language gestures. These gestures are realistic and aesthetically acceptable and can be recognized and understood by Deaf people.South Afric

Generating realistic, animated human gestures in order to model, analyse and recognize Irish Sign Language

The aim of this thesis is to generate a gesture recognition system which can recognize several signs of Irish Sign Language (ISL). This project is divided into three parts. The first part provides background information on ISL. An overview of the ISL structure is a prerequisite to identifying and understanding the difficulties encountered in the development of a recognition system. The second part involves the generation of a data repository: synthetic and real-time video. Initially the synthetic data is created in a 3D animation package in order to simplify the creation of motion variations of the animated signer. The animation environment in our implementation allows for the generation of different versions of the same gesture with slight variations in the parameters of the motion. Secondly a database of ISL real-time video was created. This database contains 1400 different signs, including motion variation in each gesture. The third part details step by step my novel classification system and the associated prototype recognition system. The classification system is constructed as a decision tree to identify each sign uniquely. The recognition system is based on only one component of the classification system and has been implemented as a Hidden Markov Model (HMM)

Using formal logic to represent sign language phonetics in semi-automatic annotation tasks

This thesis presents a formal framework for the representation of Signed Languages (SLs), the languages of Deaf communities, in semi-automatic recognition tasks. SLs are complex visio-gestural communication systems; by using corporal gestures, signers achieve the same level of expressivity held by sound-based languages like English or French. However, unlike these, SL morphemes correspond to complex sequences of highly specific body postures, interleaved with postural changes: during signing, signers use several parts of their body simultaneously in order to combinatorially build phonemes. This situation, paired with an extensive use of the three-dimensional space, make them difficult to represent with tools already existent in Natural Language Processing (NLP) of vocal languages. For this reason, the current work presents the development of a formal representation framework, intended to transform SL video repositories (corpus) into an intermediate representation layer, where automatic recognition algorithms can work under better conditions. The main idea is that corpora can be described with a specialized Labeled Transition System (LTS), which can then be annotated with logic formulae for its study. A multi-modal logic was chosen as the basis of the formal language: the Propositional Dynamic Logic (PDL). This logic was originally created to specify and prove properties on computer programs. In particular, PDL uses the modal operators [a] and to denote necessity and possibility, respectively. For SLs, a particular variant based on the original formalism was developed: the PDL for Sign Language (PDLSL). With the PDLSL, body articulators (like the hands or head) are interpreted as independent agents; each articulator has its own set of valid actions and propositions, and executes them without influence from the others. The simultaneous execution of different actions by several articulators yield distinct situations, which can be searched over an LTS with formulae, by using the semantic rules of the logic. Together, the use of PDLSL and the proposed specialized data structures could help curb some of the current problems in SL study; notably the heterogeneity of corpora and the lack of automatic annotation aids. On the same vein, this may not only increase the size of the available datasets, but even extend previous results to new corpora; the framework inserts an intermediate representation layer which can serve to model any corpus, regardless of its technical limitations. With this, annotations is possible by defining with formulae the characteristics to annotate. Afterwards, a formal verification algorithm may be able to find those features in corpora, as long as they are represented as consistent LTSs. Finally, the development of the formal framework led to the creation of a semi-automatic annotator based on the presented theoretical principles. Broadly, the system receives an untreated corpus video, converts it automatically into a valid LTS (by way of some predefined rules), and then verifies human-created PDLSL formulae over the LTS. The final product, is an automatically generated sub-lexical annotation, which can be later corrected by human annotators for their use in other areas such as linguistics.Cette thèse présente le développement d'un framework formel pour la représentation des Langues de Signes (LS), les langages des communautés Sourdes, dans le cadre de la construction d'un système de reconnaissance automatique. Les LS sont de langues naturelles, qui utilisent des gestes et l'espace autour du signeur pour transmettre de l'information. Cela veut dire que, à différence des langues vocales, les morphèmes en LS ne correspondent pas aux séquences de sons; ils correspondent aux séquences de postures corporelles très spécifiques, séparés par des changements tels que de mouvements. De plus, lors du discours les signeurs utilisent plusieurs parties de leurs corps (articulateurs) simultanément, ce qui est difficile à capturer avec un système de notation écrite. Cette situation difficulté leur représentation dans de taches de Traitement Automatique du Langage Naturel (TALN). Pour ces raisons, le travail présenté dans ce document a comme objectif la construction d'une représentation abstraite de la LS; plus précisément, le but est de pouvoir représenter des collections de vidéo LS (corpus) de manière formelle. En générale, il s'agit de construire une couche de représentation intermédiaire, permettant de faire de la reconnaissance automatique indépendamment des technologies de suivi et des corpus utilisés pour la recherche. Cette couche corresponde à un système de transition d'états (STE), spécialement crée pour représenter la nature parallèle des LS. En plus, elle peut-être annoté avec de formules logiques pour son analyse, à travers de la vérification de modèles. Pour représenter les propriétés à vérifier, une logique multi-modale a été choisi : la Logique Propositionnelle Dynamique (PDL). Cette logique a été originalement crée pour la spécification de programmes. De manière plus précise, PDL permit d'utilise des opérateurs modales comme [a] et , représentant > et >, respectivement. Une variante particulaire a été développée pour les LS : la PDL pour Langue de Signes (PDLSL), qui est interprété sur des STE représentant des corpus. Avec PDLSL, chaque articulateur du corps (comme les mains et la tête) est vu comme un agent indépendant; cela veut dire que chacun a ses propres actions et propositions possibles, et qu'il peux les exécuter pour influencer une posture gestuelle. L'utilisation du framework proposé peut aider à diminuer deux problèmes importantes qui existent dans l'étude linguistique des LS : hétérogénéité des corpus et la manque des systèmes automatiques d'aide à l'annotation. De ce fait, un chercheur peut rendre exploitables des corpus existants en les transformant vers des STE. Finalement, la création de cet outil à permit l'implémentation d'un système d'annotation semi-automatique, basé sur les principes théoriques du formalisme. Globalement, le système reçoit des vidéos LS et les transforme dans un STE valide. Ensuite, un module fait de la vérification formelle sur le STE, en utilisant une base de données de formules crée par un expert en LS. Les formules représentent des propriétés lexicales à chercher dans le STE. Le produit de ce processus, est une annotation qui peut être corrigé par des utilisateurs humains, et qui est utilisable dans des domaines d'études tels que la linguistique

Using formal logic to represent sign language phonetics in semi-automatic annotation tasks

Cette thèse présente le développement d'un framework formel pour la représentation des Langues de Signes (LS), les langages des communautés Sourdes, dans le cadre de la construction d'un système de reconnaissance automatique. Les LS sont de langues naturelles, qui utilisent des gestes et l'espace autour du signeur pour transmettre de l'information. Cela veut dire que, à différence des langues vocales, les morphèmes en LS ne correspondent pas aux séquences de sons; ils correspondent aux séquences de postures corporelles très spécifiques, séparés par des changements tels que de mouvements. De plus, lors du discours les signeurs utilisent plusieurs parties de leurs corps (articulateurs) simultanément, ce qui est difficile à capturer avec un système de notation écrite. Cette situation difficulté leur représentation dans de taches de Traitement Automatique du Langage Naturel (TALN). Pour ces raisons, le travail présenté dans ce document a comme objectif la construction d'une représentation abstraite de la LS; plus précisément, le but est de pouvoir représenter des collections de vidéo LS (corpus) de manière formelle. En générale, il s'agit de construire une couche de représentation intermédiaire, permettant de faire de la reconnaissance automatique indépendamment des technologies de suivi et des corpus utilisés pour la recherche. Cette couche corresponde à un système de transition d'états (STE), spécialement crée pour représenter la nature parallèle des LS. En plus, elle peut-être annoté avec de formules logiques pour son analyse, à travers de la vérification de modèles. Pour représenter les propriétés à vérifier, une logique multi-modale a été choisi : la Logique Propositionnelle Dynamique (PDL). Cette logique a été originalement crée pour la spécification de programmes. De manière plus précise, PDL permit d'utilise des opérateurs modales comme [a] et , représentant > et >, respectivement. Une variante particulaire a été développée pour les LS : la PDL pour Langue de Signes (PDLSL), qui est interprété sur des STE représentant des corpus. Avec PDLSL, chaque articulateur du corps (comme les mains et la tête) est vu comme un agent indépendant; cela veut dire que chacun a ses propres actions et propositions possibles, et qu'il peux les exécuter pour influencer une posture gestuelle. L'utilisation du framework proposé peut aider à diminuer deux problèmes importantes qui existent dans l'étude linguistique des LS : hétérogénéité des corpus et la manque des systèmes automatiques d'aide à l'annotation. De ce fait, un chercheur peut rendre exploitables des corpus existants en les transformant vers des STE. Finalement, la création de cet outil à permit l'implémentation d'un système d'annotation semi-automatique, basé sur les principes théoriques du formalisme. Globalement, le système reçoit des vidéos LS et les transforme dans un STE valide. Ensuite, un module fait de la vérification formelle sur le STE, en utilisant une base de données de formules crée par un expert en LS. Les formules représentent des propriétés lexicales à chercher dans le STE. Le produit de ce processus, est une annotation qui peut être corrigé par des utilisateurs humains, et qui est utilisable dans des domaines d'études tels que la linguistique.This thesis presents a formal framework for the representation of Signed Languages (SLs), the languages of Deaf communities, in semi-automatic recognition tasks. SLs are complex visio-gestural communication systems; by using corporal gestures, signers achieve the same level of expressivity held by sound-based languages like English or French. However, unlike these, SL morphemes correspond to complex sequences of highly specific body postures, interleaved with postural changes: during signing, signers use several parts of their body simultaneously in order to combinatorially build phonemes. This situation, paired with an extensive use of the three-dimensional space, make them difficult to represent with tools already existent in Natural Language Processing (NLP) of vocal languages. For this reason, the current work presents the development of a formal representation framework, intended to transform SL video repositories (corpus) into an intermediate representation layer, where automatic recognition algorithms can work under better conditions. The main idea is that corpora can be described with a specialized Labeled Transition System (LTS), which can then be annotated with logic formulae for its study. A multi-modal logic was chosen as the basis of the formal language: the Propositional Dynamic Logic (PDL). This logic was originally created to specify and prove properties on computer programs. In particular, PDL uses the modal operators [a] and to denote necessity and possibility, respectively. For SLs, a particular variant based on the original formalism was developed: the PDL for Sign Language (PDLSL). With the PDLSL, body articulators (like the hands or head) are interpreted as independent agents; each articulator has its own set of valid actions and propositions, and executes them without influence from the others. The simultaneous execution of different actions by several articulators yield distinct situations, which can be searched over an LTS with formulae, by using the semantic rules of the logic. Together, the use of PDLSL and the proposed specialized data structures could help curb some of the current problems in SL study; notably the heterogeneity of corpora and the lack of automatic annotation aids. On the same vein, this may not only increase the size of the available datasets, but even extend previous results to new corpora; the framework inserts an intermediate representation layer which can serve to model any corpus, regardless of its technical limitations. With this, annotations is possible by defining with formulae the characteristics to annotate. Afterwards, a formal verification algorithm may be able to find those features in corpora, as long as they are represented as consistent LTSs. Finally, the development of the formal framework led to the creation of a semi-automatic annotator based on the presented theoretical principles. Broadly, the system receives an untreated corpus video, converts it automatically into a valid LTS (by way of some predefined rules), and then verifies human-created PDLSL formulae over the LTS. The final product, is an automatically generated sub-lexical annotation, which can be later corrected by human annotators for their use in other areas such as linguistics

Real-time Immersive human-computer interaction based on tracking and recognition of dynamic hand gestures

With fast developing and ever growing use of computer based technologies, human-computer interaction (HCI) plays an increasingly pivotal role. In virtual reality (VR), HCI technologies provide not only a better understanding of three-dimensional shapes and spaces, but also sensory immersion and physical interaction. With the hand based HCI being a key HCI modality for object manipulation and gesture based communication, challenges are presented to provide users a natural, intuitive, effortless, precise, and real-time method for HCI based on dynamic hand gestures, due to the complexity of hand postures formed by multiple joints with high degrees-of-freedom, the speed of hand movements with highly variable trajectories and rapid direction changes, and the precision required for interaction between hands and objects in the virtual world. Presented in this thesis is the design and development of a novel real-time HCI system based on a unique combination of a pair of data gloves based on fibre-optic curvature sensors to acquire finger joint angles, a hybrid tracking system based on inertia and ultrasound to capture hand position and orientation, and a stereoscopic display system to provide an immersive visual feedback. The potential and effectiveness of the proposed system is demonstrated through a number of applications, namely, hand gesture based virtual object manipulation and visualisation, hand gesture based direct sign writing, and hand gesture based finger spelling. For virtual object manipulation and visualisation, the system is shown to allow a user to select, translate, rotate, scale, release and visualise virtual objects (presented using graphics and volume data) in three-dimensional space using natural hand gestures in real-time. For direct sign writing, the system is shown to be able to display immediately the corresponding SignWriting symbols signed by a user using three different signing sequences and a range of complex hand gestures, which consist of various combinations of hand postures (with each finger open, half-bent, closed, adduction and abduction), eight hand orientations in horizontal/vertical plans, three palm facing directions, and various hand movements (which can have eight directions in horizontal/vertical plans, and can be repetitive, straight/curve, clockwise/anti-clockwise). The development includes a special visual interface to give not only a stereoscopic view of hand gestures and movements, but also a structured visual feedback for each stage of the signing sequence. An excellent basis is therefore formed to develop a full HCI based on all human gestures by integrating the proposed system with facial expression and body posture recognition methods. Furthermore, for finger spelling, the system is shown to be able to recognise five vowels signed by two hands using the British Sign Language in real-time

Data-driven machine translation for sign languages

This thesis explores the application of data-driven machine translation (MT) to sign languages (SLs). The provision of an SL MT system can facilitate communication between Deaf and hearing people by translating information into the native and preferred language of the individual. We begin with an introduction to SLs, focussing on Irish Sign Language - the native language of the Deaf in Ireland. We describe their linguistics and mechanics including similarities and differences with spoken languages. Given the lack of a formalised written form of these languages, an outline of annotation formats is discussed as well as the issue of data collection. We summarise previous approaches to SL MT, highlighting the pros and cons of each approach. Initial experiments in the novel area of example-based MT for SLs are discussed and an overview of the problems that arise when automatically translating these manual-visual languages is given. Following this we detail our data-driven approach, examining the MT system used and modifications made for the treatment of SLs and their annotation. Through sets of automatically evaluated experiments in both language directions, we consider the merits of data-driven MT for SLs and outline the mainstream evaluation metrics used. To complete the translation into SLs, we discuss the addition and manual evaluation of a signing avatar for real SL output

Sign language research, uses and practices : a Belgian perspective

The title of the book, Sign Language Research, Uses And Practices, wants to reflect both the fact that the papers included relate to sign language research on structure, uses and practices, and our belief that sign linguistics cannot be separated from Deaf community practices, including practices in education and interpretation. Furthermore, in our opinion, there is a (strong) relationship between the uses and practices of sign languages on the one hand, and sign language structure and research on the other hand. We will briefly explain what we mean by presenting some information related to Flemish Sign Language (Vlaamse Gebarentaal or VGT) and French Belgian Sign Language (Langue des signes de Belgique francophone or LSFB), the sign languages used in Belgium