Design, development and field evaluation of a Spanish into sign language translation system

This paper describes the design, development and field evaluation of a machine translation system from Spanish to Spanish Sign Language (LSE: Lengua de Signos Española). The developed system focuses on helping Deaf people when they want to renew their Driver’s License. The system is made up of a speech recognizer (for decoding the spoken utterance into a word sequence), a natural language translator (for converting a word sequence into a sequence of signs belonging to the sign language), and a 3D avatar animation module (for playing back the signs). For the natural language translator, three technological approaches have been implemented and evaluated: an example-based strategy, a rule-based translation method and a statistical translator. For the final version, the implemented language translator combines all the alternatives into a hierarchical structure. This paper includes a detailed description of the field evaluation. This evaluation was carried out in the Local Traffic Office in Toledo involving real government employees and Deaf people. The evaluation includes objective measurements from the system and subjective information from questionnaires. The paper details the main problems found and a discussion on how to solve them (some of them specific for LSE)

Blinking in Human Communicative Behaviour and it's Reproduction in Artificial Agents

A significant year-on-year rise in the creation and sales of personal and domestic robotic systems and the development of online embodied communicative agents (ECAs) has in parallel seen an increase in end-users from the public domain interacting with these systems. A number of these robotic/ECA systems are defined as social, whereby they are physically designed to resemble the bodily structure of a human and behaviorally designed to exist within human social surroundings. Their behavioural design is especially important with respect to communication as it is commonly stated that for any social robotic/ECA system to be truly useful within its role, it will need to be able to effectively communicate with its human users. Currently however, the act of a human user instructing a social robotic/ECA system to perform a task highlights many areas of contention in human communication understanding. Commonly, social robotic/ECA systems are embedded with either non-human-like communication interfaces or deficient imitative human communication interfaces, neither of which reach the levels of communicative interaction expected by human users, leading to communication difficulties which in turn create negative association with the social robotic/ECA system in its users. These communication issues lead to a strong requirement for the development of more effective imitative human communication behaviours within these systems. This thesis presents findings from our research into human non-verbal facial behaviour in communication. The objective of the work was to improve communication grounding between social robotic/ECA systems and their human users through the conceptual design of a computational system of human non-verbal facial behaviour (which in human-human communicative behaviour is shown to carry in the range of 55% of the intended semantic meaning of a transferred message) and the development of a highly accurate computational model of human blink behaviour and a computational model of physiological saccadic eye movement in human-human communication, enriching the human-like properties of the facial non-verbal communicative feedback expressed by the social robotic/ECA system. An enhanced level of interaction would likely be achieved, leading to increased empathic response from the user and an improved chance of a satisfactory communicative conclusion to a user’s task requirement instructions. The initial focus of the work was in the capture, transcription and analysis of common human non-verbal facial behavioural traits within human-human communication, linked to the expression of mental communicative states of understanding, uncertainty, misunderstanding and thought. Facial Non-Verbal behaviour data was collected and transcribed from twelve participants (six female) through a dialogue-based communicative interaction. A further focus was the analysis of blink co-occurrence with other traits of human-human communicative non-verbal facial behaviour and the capture of saccadic eye movement at common proxemic distances. From these data analysis tasks, the computational models of human blink behaviour and saccadic eye movement behaviour whilst listening / speaking within human-human communication were designed and then implemented within the LightHead social robotic system. Human-based studies on the perception of naïve users of the imitative probabilistic computational blink model performance on the LightHead robotic system are presented and the results discussed. The thesis concludes on the impact of the work along with suggestions for further studies towards the improvement of the important task of achieving seamless interactive communication between social robotic/ECA systems and their human users

MULTI-MODAL TASK INSTRUCTIONS TO ROBOTS BY NAIVE USERS

This thesis presents a theoretical framework for the design of user-programmable robots. The objective of the work is to investigate multi-modal unconstrained natural instructions given to robots in order to design a learning robot. A corpus-centred approach is used to design an agent that can reason, learn and interact with a human in a natural unconstrained way. The corpus-centred design approach is formalised and developed in detail. It requires the developer to record a human during interaction and analyse the recordings to find instruction primitives. These are then implemented into a robot. The focus of this work has been on how to combine speech and gesture using rules extracted from the analysis of a corpus. A multi-modal integration algorithm is presented, that can use timing and semantics to group, match and unify gesture and language. The algorithm always achieves correct pairings on a corpus and initiates questions to the user in ambiguous cases or missing information. The domain of card games has been investigated, because of its variety of games which are rich in rules and contain sequences. A further focus of the work is on the translation of rule-based instructions. Most multi-modal interfaces to date have only considered sequential instructions. The combination of frame-based reasoning, a knowledge base organised as an ontology and a problem solver engine is used to store these rules. The understanding of rule instructions, which contain conditional and imaginary situations require an agent with complex reasoning capabilities. A test system of the agent implementation is also described. Tests to confirm the implementation by playing back the corpus are presented. Furthermore, deployment test results with the implemented agent and human subjects are presented and discussed. The tests showed that the rate of errors that are due to the sentences not being defined in the grammar does not decrease by an acceptable rate when new grammar is introduced. This was particularly the case for complex verbal rule instructions which have a large variety of being expressed

The Role of Emotional and Facial Expression in Synthesised Sign Language Avatars

This thesis explores the role that underlying emotional facial expressions might have in regards to understandability in sign language avatars. Focusing specifically on Irish Sign Language (ISL), we examine the Deaf community’s requirement for a visual-gestural language as well as some linguistic attributes of ISL which we consider fundamental to this research. Unlike spoken language, visual-gestural languages such as ISL have no standard written representation. Given this, we compare current methods of written representation for signed languages as we consider: which, if any, is the most suitable transcription method for the medical receptionist dialogue corpus. A growing body of work is emerging from the field of sign language avatar synthesis. These works are now at a point where they can benefit greatly from introducing methods currently used in the field of humanoid animation and, more specifically, the application of morphs to represent facial expression. The hypothesis underpinning this research is: augmenting an existing avatar (eSIGN) with various combinations of the 7 widely accepted universal emotions identified by Ekman (1999) to deliver underlying facial expressions, will make that avatar more human-like. This research accepts as true that this is a factor in improving usability and understandability for ISL users. Using human evaluation methods (Huenerfauth, et al., 2008) the research compares an augmented set of avatar utterances against a baseline set with regards to 2 key areas: comprehension and naturalness of facial configuration. We outline our approach to the evaluation including our choice of ISL participants, interview environment, and evaluation methodology. Remarkably, the results of this manual evaluation show that there was very little difference between the comprehension scores of the baseline avatars and those augmented withEFEs. However, after comparing the comprehension results for the synthetic human avatar “Anna” against the caricature type avatar “Luna”, the synthetic human avatar Anna was the clear winner. The qualitative feedback allowed us an insight into why comprehension scores were not higher in each avatar and we feel that this feedback will be invaluable to the research community in the future development of sign language avatars. Other questions asked in the evaluation focused on sign language avatar technology in a more general manner. Significantly, participant feedback in regard to these questions indicates a rise in the level of literacy amongst Deaf adults as a result of mobile technology

Speech to sign language translation system for Spanish

This paper describes the development of and the first experiments in a Spanish to sign language translation system in a real domain. The developed system focuses on the sentences spoken by an official when assisting people applying for, or renewing their Identity Card. The system translates official explanations into Spanish Sign Language (LSE: Lengua de Signos Espan¿ola) for Deaf people. The translation system is made up of a speech recognizer (for decoding the spoken utterance into a word sequence), a natural language translator (for converting a word sequence into a sequence of signs belonging to the sign language), and a 3D avatar animation module (for playing back the hand movements). Two proposals for natural language translation have been evaluated: a rule-based translation module (that computes sign confidence measures from the word confidence measures obtained in the speech recognition module) and a statistical translation module (in this case, parallel corpora were used for training the statistical model). The best configuration reported 31.6% SER (Sign Error Rate) and 0.5780 BLEU (BiLingual Evaluation Understudy). The paper also describes the eSIGN 3D avatar animation module (considering the sign confidence), and the limitations found when implementing a strategy for reducing the delay between the spoken utterance and the sign sequence animation

Prosody and Kinesics Based Co-analysis Towards Continuous Gesture Recognition

The aim of this study is to develop a multimodal co-analysis framework for continuous gesture recognition by exploiting prosodic and kinesics manifestation of natural communication. Using this framework, a co-analysis pattern between correlating components is obtained. The co-analysis pattern is clustered using K-means clustering to determine how well the pattern distinguishes the gestures. Features of the proposed approach that differentiate it from the other models are its less susceptibility to idiosyncrasies, its scalability, and simplicity. The experiment was performed on Multimodal Annotated Gesture Corpus (MAGEC) that we created for research on understanding non-verbal communication community, particularly the gestures

Computer vision methods for unconstrained gesture recognition in the context of sign language annotation

Cette thèse porte sur l'étude des méthodes de vision par ordinateur pour la reconnaissance de gestes naturels dans le contexte de l'annotation de la Langue des Signes. La langue des signes (LS) est une langue gestuelle développée par les sourds pour communiquer. Un énoncé en LS consiste en une séquence de signes réalisés par les mains, accompagnés d'expressions du visage et de mouvements du haut du corps, permettant de transmettre des informations en parallèles dans le discours. Même si les signes sont définis dans des dictionnaires, on trouve une très grande variabilité liée au contexte lors de leur réalisation. De plus, les signes sont souvent séparés par des mouvements de co-articulation. Cette extrême variabilité et l'effet de co-articulation représentent un problème important dans les recherches en traitement automatique de la LS. Il est donc nécessaire d'avoir de nombreuses vidéos annotées en LS, si l'on veut étudier cette langue et utiliser des méthodes d'apprentissage automatique. Les annotations de vidéo en LS sont réalisées manuellement par des linguistes ou experts en LS, ce qui est source d'erreur, non reproductible et extrêmement chronophage. De plus, la qualité des annotations dépend des connaissances en LS de l'annotateur. L'association de l'expertise de l'annotateur aux traitements automatiques facilite cette tâche et représente un gain de temps et de robustesse. Le but de nos recherches est d'étudier des méthodes de traitement d'images afin d'assister l'annotation des corpus vidéo: suivi des composantes corporelles, segmentation des mains, segmentation temporelle, reconnaissance de gloses. Au cours de cette thèse nous avons étudié un ensemble de méthodes permettant de réaliser l'annotation en glose. Dans un premier temps, nous cherchons à détecter les limites de début et fin de signe. Cette méthode d'annotation nécessite plusieurs traitements de bas niveau afin de segmenter les signes et d'extraire les caractéristiques de mouvement et de forme de la main. D'abord nous proposons une méthode de suivi des composantes corporelles robuste aux occultations basée sur le filtrage particulaire. Ensuite, un algorithme de segmentation des mains est développé afin d'extraire la région des mains même quand elles se trouvent devant le visage. Puis, les caractéristiques de mouvement sont utilisées pour réaliser une première segmentation temporelle des signes qui est par la suite améliorée grâce à l'utilisation de caractéristiques de forme. En effet celles-ci permettent de supprimer les limites de segmentation détectées en milieu des signes. Une fois les signes segmentés, on procède à l'extraction de caractéristiques visuelles pour leur reconnaissance en termes de gloses à l'aide de modèles phonologiques. Nous avons évalué nos algorithmes à l'aide de corpus internationaux, afin de montrer leur avantages et limitations. L'évaluation montre la robustesse de nos méthodes par rapport à la dynamique et le grand nombre d'occultations entre les différents membres. L'annotation résultante est indépendante de l'annotateur et représente un gain de robustese important.This PhD thesis concerns the study of computer vision methods for the automatic recognition of unconstrained gestures in the context of sign language annotation. Sign Language (SL) is a visual-gestural language developed by deaf communities. Continuous SL consists on a sequence of signs performed one after another involving manual and non-manual features conveying simultaneous information. Even though standard signs are defined in dictionaries, we find a huge variability caused by the context-dependency of signs. In addition signs are often linked by movement epenthesis which consists on the meaningless gesture between signs. The huge variability and the co-articulation effect represent a challenging problem during automatic SL processing. It is necessary to have numerous annotated video corpus in order to train statistical machine translators and study this language. Generally the annotation of SL video corpus is manually performed by linguists or computer scientists experienced in SL. However manual annotation is error-prone, unreproducible and time consuming. In addition de quality of the results depends on the SL annotators knowledge. Associating annotator knowledge to image processing techniques facilitates the annotation task increasing robustness and speeding up the required time. The goal of this research concerns on the study and development of image processing technique in order to assist the annotation of SL video corpus: body tracking, hand segmentation, temporal segmentation, gloss recognition. Along this PhD thesis we address the problem of gloss annotation of SL video corpus. First of all we intend to detect the limits corresponding to the beginning and end of a sign. This annotation method requires several low level approaches for performing temporal segmentation and for extracting motion and hand shape features. First we propose a particle filter based approach for robustly tracking hand and face robust to occlusions. Then a segmentation method for extracting hand when it is in front of the face has been developed. Motion is used for segmenting signs and later hand shape is used to improve the results. Indeed hand shape allows to delete limits detected in the middle of a sign. Once signs have been segmented we proceed to the gloss recognition using lexical description of signs. We have evaluated our algorithms using international corpus, in order to show their advantages and limitations. The evaluation has shown the robustness of the proposed methods with respect to high dynamics and numerous occlusions between body parts. Resulting annotation is independent on the annotator and represents a gain on annotation consistency