Sign language

Sign languages (also known as signed languages) are languages that use the visual-manual modality to convey meaning, instead of spoken words. Sign languages are expressed through manual articulation in combination with non-manual markers. Sign languages are full-fledged natural languages with their own grammar and lexicon. Sign languages are not universal and are usually not mutually intelligible, although there are also similarities among different sign languages. Linguists consider both spoken and signed communication to be types of natural language, meaning that both emerged through an abstract, protracted aging process and evolved over time without meticulous planning. This is supported by the fact that there is substantial overlap between the neural substrates of sign and spoken language processing, despite the obvious differences in modality. Sign language should not be confused with body language, a type of nonverbal communication. Wherever communities of d

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ACM2020

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

An HMM Approach with Inherent Model Selection for Sign Language and Gesture Recognition

Oya Aran, Sandrine Tornay

HMMs have been the one of the first models to be applied for sign recognition and have become the baseline models due to their success in modeling sequential and multivariate data. Despite the extensive use of HMMs for sign recognition, determining the HMM structure has still remained as a challenge, especially when the number of signs to be modeled is high. In this work, we present a continuous HMM framework for modeling and recognizing isolated signs, which inherently performs model selection to optimize the number of states for each sign separately during recognition. Our experiments on three different datasets, namely, German sign language DGS dataset, Turkish sign language HospiSign dataset and Chalearn14 dataset show that the proposed approach achieves better sign language or gesture recognition systems in comparison to the approach of selecting or presetting the number of HMM states based on k-means, and yields systems that perform competitive to the case where the number of states are determined based on the test set performance.

ACL2020

CS-207: System oriented programming

Cours de programmation en langage C se focalisant sur l'utilisation des ressources système, en particulier la gestion de la mémoire (pointeurs).

EE-608: Deep Learning For Natural Language Processing

The Deep Learning for NLP course provides an overview of neural network based methods applied to text. The focus is on models particularly suited to the properties of human language, such as categorical, unbounded, and structured representations, and very large input and output vocabularies.

HUM-213: Cognitive psychology A

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Language

Language is a structured system of communication that consists of grammar and vocabulary. It is the primary means by which humans convey meaning, both in spoken and written forms, and may also be conv

Linguistics

Linguistics is the scientific study of language. The modern-day scientific study of linguistics takes all aspects of language into account — i.e., the cognitive, the social, the cultural, the psycholo

Phonology

Phonology is the branch of linguistics that studies how languages or dialects systematically organize their phones or, for sign languages, their constituent parts of signs. The term can also refer s