Oscar Koller

• Electrical Engineering, M. Sc. / Diplom
• Research Assistant at RWTH Aachen University

Sign language detection, identifying if someone is signing or not, is becoming crucially important for its applications in remote conferencing software and for selecting useful sign data for training sign language recognition or translation tasks. We argue that the current benchmark data sets for sign language detection estimate overly positive res...

This paper presents the results of the First WMT Shared Task on Sign Language Translation (WMT-SLT22) 1. This shared task is concerned with automatic translation between signed and spoken 2 languages. The task is novel in the sense that it requires processing visual information (such as video frames or human pose estimation) beyond the well-known p...

This paper describes Microsoft's submission to the first shared task on sign language translation at WMT 2022, a public competition tackling sign language to spoken language translation for Swiss German sign language. The task is very challenging due to data scarcity and an unprecedented vocabulary size of more than 20k words on the target side. Mo...

Sign language datasets are essential to developing many sign language technologies. In particular, datasets are required for training artificial intelligence (AI) and machine learning (ML) systems. Though the idea of using AI/ML for sign languages is not new, technology has now advanced to a point where developing such sign language technologies is...

This paper describes the winning approach in the public SwissText 2021 competition on dialect recognition and translation of Swiss German speech to standard German text. Swiss German refers to the multitude of Alemannic dialects spoken in the German-speaking parts of Switzerland. Swiss German differs significantly from standard German in pronunciat...

Sign languages use multiple asynchronous information channels (articulators), not just the hands but also the face and body, which computational approaches often ignore. In this paper we tackle the multi-articulatory sign language translation task and propose a novel multi-channel transformer architecture. The proposed architecture allows both the...

Sign languages use multiple asynchronous information channels (articulators), not just the hands but also the face and body, which computational approaches often ignore. In this paper we tackle the multi-articulatory sign language translation task and propose a novel multi-channel transformer architecture. The proposed architecture allows both the...

This work presents a meta study covering around 300 published sign language recognition papers with over 400 experimental results. It includes most papers between the start of the field in 1983 and 2020. Additionally, it covers a fine-grained analysis on over 25 studies that have compared their recognition approaches on \phoenix 2014, the standard...

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 architect...

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 architect...

Sign languages use multiple asynchronous information channels (articulators), not just the hands but also the face and body, which computational approaches often ignore. In this paper we tackle the multi-articulatory sign language translation task and propose a novel multi-channel transformer architecture. The proposed architecture allows both the...

Developing successful sign language recognition, generation, and translation systems requires expertise in a wide range of fields, including computer vision, computer graphics, natural language processing, human-computer interaction, linguistics, and Deaf culture. Despite the need for deep interdisciplinary knowledge, existing research occurs in se...

Developing successful sign language recognition, generation, and translation systems requires expertise in a wide range of fields, including computer vision, computer graphics, natural language processing, human-computer interaction, linguistics, and Deaf culture. Despite the need for deep interdisciplinary knowledge, existing research occurs in se...

In this work we present a new approach to the field of weakly supervised learning in the video domain. Our method is relevant to sequence learning problems which can be split up into sub-problems that occur in parallel. Here, we experiment with sign language data. The approach exploits sequence constraints within each independent stream and combine...

Computer Vision has been improved significantly in the past few decades. It has enabled machine to do many human tasks. However, the real challenge is in enabling machine to carry out tasks that an average human does not have the skills for. One such challenge that we have tackled in this paper is providing accessibility for deaf individual by prov...

This manuscript introduces the end-to-end embedding of a CNN into a HMM, while interpreting the outputs of the CNN in a Bayesian framework. The hybrid CNN-HMM combines the strong discriminative abilities of CNNs with the sequence modelling capabilities of HMMs. Most current approaches in the field of gesture and sign language recognition disregard...

Sign Language Recognition (SLR) has been an active research field for the last two decades. However, most research to date has considered SLR as a naive gesture recognition problem. SLR seeks to recognize a sequence of continuous signs but neglects the underlying rich grammatical and linguistic structures of sign language that differ from spoken la...

We propose a novel deep learning approach to solve simultaneous alignment and recognition problems (referred to as “Sequence-to-sequence” learning). We decompose the problem into a series of specialised expert systems referred to as SubUNets. The spatio-temporal relationships between these SubUNets are then modelled to solve the task, while remaini...

This work presents an iterative realignment approach applicable to visual sequence labelling tasks such as gesture recognition, activity recognition and continuous sign language recognition. Previous methods dealing with video data usually rely on given frame labels to train their clas-sifiers. Looking at recent data sets, these labels often tend t...

In this paper, we propose using 3D Convolutional Neural Networks for large scale user-independent continuous gesture recognition. We have trained an end-to-end deep network for continuous gesture recognition (jointly learning both the feature representation and the classifier). The network performs three-dimensional (i.e. space-time) convolutions t...

This paper introduces the end-to-end embedding of a CNN into a HMM, while interpreting the outputs of the CNN in a Bayesian fashion. The hybrid CNN-HMM combines the strong discriminative abilities of CNNs with the sequence modelling capabilities of HMMs. Most current approaches in the field of gesture and sign language recognition disregard the nec...

This work presents a new approach to learning a frame-based classifier on weakly labelled sequence data by embedding a CNN within an iterative EM algorithm. This allows the CNN to be trained on a vast number of example images when only loose sequence level information is available for the source videos. Although we demonstrate this in the context o...

This work presents our recent advances in the field of automatic processing of sign language corpora targeting continuous sign language recognition. We demonstrate how generic annotations at the articulator level, such as HamNoSys, can be exploited to learn subunit classifiers. Specifically, we explore cross-language-subunits of the hand orientatio...

This work presents a statistical recognition approach performing large vocabulary continuous sign language recognition across different signers. Automatic sign language recognition is currently evolving from artificial lab-generated data to 'real-life' data. To the best of our knowledge, this is the first time system design on a large data set with...

This paper deals with robust modelling of mouth shapes in the context of sign language recognition using deep con-volutional neural networks. Sign language mouth shapes are difficult to annotate and thus hardly any publicly available annotations exist. As such, this work exploits related information sources as weak supervision. Humans mainly look a...

This work presents a framework to recognise signer independent mouthings in continuous sign language, with no manual annotations needed. Mouthings represent lip-movements that correspond to pronunciations of words or parts of them during signing. Research on sign language recognition has focused extensively on the hands as features. But sign langua...

This paper tackles the problem of spotting a set of signs occuring in videos with sequences of signs. To achieve this, we propose to model the spatio-temporal signatures of a sign using an extension of sequential patterns that contain temporal intervals called Sequential Interval Patterns (SIP). We then propose a novel multi-class classifier that o...

Sign language-to-text translation systems are similar to spo-ken language translation systems in that they consist of a recognition phase and a translation phase. First, the video of a person signing is transformed into a transcription of the signs, which is then translated into the text of a spoken language. One distinctive feature of sign languag...

Sign languages comprise parallel aspects and use several mo-dalities to form a sign but so far it is not clear how to best combine these modalities in the context of statistical sign language recognition. We in-vestigate early combination of features, late fusion of decisions, as well as synchronous combination on the hidden Markov model state leve...

We propose a method to generate linguistically meaningful subunits in a fully automated fashion for sign language corpora. The ability to automate the process of subunit annotation has profound effects on the data available for training sign language recognition systems. The approach is based on the idea that subunits are shared among different sig...

Best language recognition performance is commonly obtained by fusing the scores of several heterogeneous systems. Regardless the fusion approach, it is assumed that different systems may contribute complementary information, either because they are developed on different datasets, or because they use different features or different modeling approac...

This paper presents a description of INESC-ID's Spoken Language Systems Laboratory (L 2 F) Language Verification systems submitted to the ALBAYZIN-2010 evaluation. The primary submission consists of the fusion of six individual sub-systems: one Gaussian supervector approach with support vec-tor machines that relies on the acoustic characteristics e...

This paper presents a Variety IDentification (VID) approach and its application to broadcast news transcription for Portuguese. The phonotactic VID system, based on Phone Recognition and Language Modelling, focuses on a single tokenizer that combines distinctive knowledge about differences between the target varieties. This knowledge is introduced...