Sign Language Transformers: Joint End-to-end Sign Language Recognition and Translation

Prior work on Sign Language Translation has shown that having a mid-level sign gloss representation (effectively recognizing the individual signs) improves the translation performance drastically. In fact, the current state-of-the-art in translation requires gloss level tokenization in order to work. We introduce a novel transformer based architecture that jointly learns Continuous Sign Language Recognition and Translation while being trainable in an end-to-end manner. This is achieved by using a Connectionist Temporal Classification (CTC) loss to bind the recognition and translation problems into a single unified architecture. This joint approach does not require any ground-truth timing information, simultaneously solving two co-dependant sequence-to-sequence learning problems and leads to significant performance gains. We evaluate the recognition and translation performances of our approaches on the challenging RWTH-PHOENIX-Weather-2014T (PHOENIX14T) dataset. We report state-of-the-art sign language recognition and translation results achieved by our Sign Language Transformers. Our translation networks outperform both sign video to spoken language and gloss to spoken language translation models, in some cases more than doubling the performance (9.58 vs. 21.80 BLEU-4 Score). We also share new baseline translation results using transformer networks for several other text-to-text sign language translation tasks

Selecting Exemplar Recordings of American Sign Language Non-Manual Expressions for Animation Synthesis Based on Manual Sign Timing

Animations of sign language can increase the accessibility of information for people who are deaf or hard of hearing (DHH), but prior work has demonstrated that accurate non-manual expressions (NMEs), consisting of face and head movements, are necessary to produce linguistically accurate animations that are easy to understand. When synthesizing animation, given a sequence of signs performed on the hands (and their timing), we must select an NME performance. Given a corpus of facial motion-capture recordings of ASL sentences with annotation of the timing of signs in the recording, we investigate methods (based on word count and on delexicalized sign timing) for selecting the best NME recoding to use as a basis for synthesizing a novel animation. By comparing recordings selected using these methods to a gold-standard recording, we identify the top-performing exemplar selection method for several NME categories

A Comprehensive Approach to Automated Sign Language Translation

Many sign languages are bonafide natural languages with grammatical rules and lexicons, hence can benefit from neural machine translation methods. As significant advances are being made in natural language processing (specifically neural machine translation) and in computer vision processes, specifically image and video captioning, related methods can be further researched to boost automated sign language understanding. This is an especially challenging AI research area due to the involvement of a continuous visual-spatial modality, where meaning is often derived from context. To this end, this thesis is focused on the study and development of new computational methods and training mechanisms to enhance sign language translation in two directions, signs to texts and texts to signs. This work introduces a new, realistic phrase-level American Sign Language dataset (ASL/ ASLing), and investigates the role of different types of visual features (CNN embeddings, human body keypoints, and optical flow vectors) in translating ASL to spoken American English. Additionally, the research considers the role of multiple features for improved translation, via various fusion architectures. As an added benefit, with continuous sign language being challenging to segment, this work also explores the use of overlapping scaled visual segments, across the video, for simultaneously segmenting and translating signs. Finally, a quintessential interpreting agent not only understands sign language and translates to text, but also understands the text and translates to signs. Hence, to facilitate two-way sign language communication, i.e. visual sign to spoken language translation and spoken to visual sign language translation, a dual neural machine translation model, SignNet, is presented. Various training paradigms are investigated for improved translation, using SignNet. By exploiting the notion of similarity (and dissimilarity) of visual signs, a metric embedding learning process proved most useful in training SignNet. The resulting processes outperformed their state-of-the-art counterparts by showing noteworthy improvements in BLEU 1 - BLEU 4 scores

A Systematic Mapping of Translation-Enabling Technologies for Sign Languages

Sign languages (SL) are the first language for most deaf people. Consequently, bidirectional communication among deaf and non-deaf people has always been a challenging issue. Sign language usage has increased due to inclusion policies and general public agreement, which must then become evident in information technologies, in the many facets that comprise sign language understanding and its computational treatment. In this study, we conduct a thorough systematic mapping of translation-enabling technologies for sign languages. This mapping has considered the most recommended guidelines for systematic reviews, i.e., those pertaining software engineering, since there is a need to account for interdisciplinary areas of accessibility, human computer interaction, natural language processing, and education, all of them part of ACM (Association for Computing Machinery) computing classification system directly related to software engineering. An ongoing development of a software tool called SYMPLE (SYstematic Mapping and Parallel Loading Engine) facilitated the querying and construction of a base set of candidate studies. A great diversity of topics has been studied over the last 25 years or so, but this systematic mapping allows for comfortable visualization of predominant areas, venues, top authors, and different measures of concentration and dispersion. The systematic review clearly shows a large number of classifications and subclassifications interspersed over time. This is an area of study in which there is much interest, with a basically steady level of scientific publications over the last decade, concentrated mainly in the European continent. The publications by country, nevertheless, usually favor their local sign language.The authors thank the School of Computing and the Computer Research Center of the Technological Institute of Costa Rica for the financial support, as well as CONICIT (Consejo Nacional para Investigaciones Científicas y Tecnológicas), Costa Rica, under grant 290-2006. This work was partly supported by the Spanish Ministry of Science, Innovation, and Universities through the Project ECLIPSE-UA under Grant RTI2018-094283-B-C32 and the Project INTEGER under Grant RTI2018-094649-B-I00, and partly by the Conselleria de Educación, Investigación, Cultura y Deporte of the Community of Valencia, Spain, within the Project PROMETEO/2018/089

An Effective Hybrid Approach Based on Machine Learning Techniques for Auto-Translation: Japanese to English

In recent years machine learning techniques have been able to perform tasks previously thought impossible or impractical such as image classification and natural language translation, as such this allows for the automation of tasks previously thought only possible by humans. This research work aims to test a naïve post processing grammar correction method using a Long Short Term Memory neural network to rearrange translated sentences from Subject Object Verb to Subject Verb Object. Here machine learning based techniques are used to successfully translate works in an automated fashion rather than manually and post processing translations to increase sentiment and grammar accuracy. The implementation of the proposed methodology uses a bounding box object detection model, optical character recognition model and a natural language processing model to fully translate manga without human intervention. The grammar correction experimentation tries to fix a common problem when machines translate between two natural languages that use different ordering, in this case from Japanese Subject Object Verb to English Subject Verb Object. For this experimentation 2 sequence to sequence Long Short Term Memory neural networks were developed, a character level and a word level model using word embedding to reorder English sentences from Subject Object Verb to Subject Verb Object. The results showed that the methodology works in practice and can automate the translation process successfully

Continuous 3D Multi-Channel Sign Language Production via Progressive Transformers and Mixture Density Networks

Sign languages are multi-channel visual languages, where signers use a continuous 3D space to communicate.Sign Language Production (SLP), the automatic translation from spoken to sign languages, must embody both the continuous articulation and full morphology of sign to be truly understandable by the Deaf community. Previous deep learning-based SLP works have produced only a concatenation of isolated signs focusing primarily on the manual features, leading to a robotic and non-expressive production. In this work, we propose a novel Progressive Transformer architecture, the first SLP model to translate from spoken language sentences to continuous 3D multi-channel sign pose sequences in an end-to-end manner. Our transformer network architecture introduces a counter decoding that enables variable length continuous sequence generation by tracking the production progress over time and predicting the end of sequence. We present extensive data augmentation techniques to reduce prediction drift, alongside an adversarial training regime and a Mixture Density Network (MDN) formulation to produce realistic and expressive sign pose sequences. We propose a back translation evaluation mechanism for SLP, presenting benchmark quantitative results on the challenging PHOENIX14T dataset and setting baselines for future research. We further provide a user evaluation of our SLP model, to understand the Deaf reception of our sign pose productions

Data and methods for a visual understanding of sign languages

Signed languages are complete and natural languages used as the first or preferred mode of communication by millions of people worldwide. However, they, unfortunately, continue to be marginalized languages. Designing, building, and evaluating models that work on sign languages presents compelling research challenges and requires interdisciplinary and collaborative efforts. The recent advances in Machine Learning (ML) and Artificial Intelligence (AI) has the power to enable better accessibility to sign language users and narrow down the existing communication barrier between the Deaf community and non-sign language users. However, recent AI-powered technologies still do not account for sign language in their pipelines. This is mainly because sign languages are visual languages, that use manual and non-manual features to convey information, and do not have a standard written form. Thus, the goal of this thesis is to contribute to the development of new technologies that account for sign language by creating large-scale multimodal resources suitable for training modern data-hungry machine learning models and developing automatic systems that focus on computer vision tasks related to sign language that aims at learning better visual understanding of sign languages. Thus, in Part I, we introduce the How2Sign dataset, which is a large-scale collection of multimodal and multiview sign language videos in American Sign Language. In Part II, we contribute to the development of technologies that account for sign languages by presenting in Chapter 4 a framework called Spot-Align, based on sign spotting methods, to automatically annotate sign instances in continuous sign language. We further present the benefits of this framework and establish a baseline for the sign language recognition task on the How2Sign dataset. In addition to that, in Chapter 5 we benefit from the different annotations and modalities of the How2Sign to explore sign language video retrieval by learning cross-modal embeddings. Later in Chapter 6, we explore sign language video generation by applying Generative Adversarial Networks to the sign language domain and assess if and how well sign language users can understand automatically generated sign language videos by proposing an evaluation protocol based on How2Sign topics and English translationLes llengües de signes són llengües completes i naturals que utilitzen milions de persones de tot el món com mode de comunicació primer o preferit. Tanmateix, malauradament, continuen essent llengües marginades. Dissenyar, construir i avaluar tecnologies que funcionin amb les llengües de signes presenta reptes de recerca que requereixen d’esforços interdisciplinaris i col·laboratius. Els avenços recents en l’aprenentatge automàtic i la intel·ligència artificial (IA) poden millorar l’accessibilitat tecnològica dels signants, i alhora reduir la barrera de comunicació existent entre la comunitat sorda i les persones no-signants. Tanmateix, les tecnologies més modernes en IA encara no consideren les llengües de signes en les seves interfícies amb l’usuari. Això es deu principalment a que les llengües de signes són llenguatges visuals, que utilitzen característiques manuals i no manuals per transmetre informació, i no tenen una forma escrita estàndard. Els objectius principals d’aquesta tesi són la creació de recursos multimodals a gran escala adequats per entrenar models d’aprenentatge automàtic per a llengües de signes, i desenvolupar sistemes de visió per computador adreçats a una millor comprensió automàtica de les llengües de signes. Així, a la Part I presentem la base de dades How2Sign, una gran col·lecció multimodal i multivista de vídeos de la llengua de signes nord-americana. A la Part II, contribuïm al desenvolupament de tecnologia per a llengües de signes, presentant al capítol 4 una solució per anotar signes automàticament anomenada Spot-Align, basada en mètodes de localització de signes en seqüències contínues de signes. Després, presentem els avantatges d’aquesta solució i proporcionem uns primers resultats per la tasca de reconeixement de la llengua de signes a la base de dades How2Sign. A continuació, al capítol 5 aprofitem de les anotacions i diverses modalitats de How2Sign per explorar la cerca de vídeos en llengua de signes a partir de l’entrenament d’incrustacions multimodals. Finalment, al capítol 6, explorem la generació de vídeos en llengua de signes aplicant xarxes adversàries generatives al domini de la llengua de signes. Avaluem fins a quin punt els signants poden entendre els vídeos generats automàticament, proposant un nou protocol d’avaluació basat en les categories dins de How2Sign i la traducció dels vídeos a l’anglès escritLas lenguas de signos son lenguas completas y naturales que utilizan millones de personas de todo el mundo como modo de comunicación primero o preferido. Sin embargo, desgraciadamente, siguen siendo lenguas marginadas. Diseñar, construir y evaluar tecnologías que funcionen con las lenguas de signos presenta retos de investigación que requieren esfuerzos interdisciplinares y colaborativos. Los avances recientes en el aprendizaje automático y la inteligencia artificial (IA) pueden mejorar la accesibilidad tecnológica de los signantes, al tiempo que reducir la barrera de comunicación existente entre la comunidad sorda y las personas no signantes. Sin embargo, las tecnologías más modernas en IA todavía no consideran las lenguas de signos en sus interfaces con el usuario. Esto se debe principalmente a que las lenguas de signos son lenguajes visuales, que utilizan características manuales y no manuales para transmitir información, y carecen de una forma escrita estándar. Los principales objetivos de esta tesis son la creación de recursos multimodales a gran escala adecuados para entrenar modelos de aprendizaje automático para lenguas de signos, y desarrollar sistemas de visión por computador dirigidos a una mejor comprensión automática de las lenguas de signos. Así, en la Parte I presentamos la base de datos How2Sign, una gran colección multimodal y multivista de vídeos de lenguaje la lengua de signos estadounidense. En la Part II, contribuimos al desarrollo de tecnología para lenguas de signos, presentando en el capítulo 4 una solución para anotar signos automáticamente llamada Spot-Align, basada en métodos de localización de signos en secuencias continuas de signos. Después, presentamos las ventajas de esta solución y proporcionamos unos primeros resultados por la tarea de reconocimiento de la lengua de signos en la base de datos How2Sign. A continuación, en el capítulo 5 aprovechamos de las anotaciones y diversas modalidades de How2Sign para explorar la búsqueda de vídeos en lengua de signos a partir del entrenamiento de incrustaciones multimodales. Finalmente, en el capítulo 6, exploramos la generación de vídeos en lengua de signos aplicando redes adversarias generativas al dominio de la lengua de signos. Evaluamos hasta qué punto los signantes pueden entender los vídeos generados automáticamente, proponiendo un nuevo protocolo de evaluación basado en las categorías dentro de How2Sign y la traducción de los vídeos al inglés escrito.Teoria del Senyal i Comunicacion