The present report is submitted in response to General Assembly resolution 61/106, by which the Assembly adopted the Convention on the Rights of Persons with Disabilities and the Optional Protocol thereto. As requested by the Assembly, a brief overview of the status of the Convention as at 3 August 2007 is presented. The report also contains a brief description of technical arrangements on staff and facilities made necessary for the effective performance of the functions of the Conference of States Parties and the Committee under the Convention and the Optional Protocol, and a description on the progressive implementation of standards and guidelines for the accessibility of facilities and services of the United Nations system. Recent efforts of the United Nations and agencies to disseminate information on the Convention and the Optional Protocol are also described.

わかりやすい障害者の権利条約 : 知的障害のある人の権利のために = Convention on the rights of persons with disabilities

我的台灣, 看見心靈的故鄉 =2009林磐聳藝術與設計展

▶ Addresses the accessibility, usability, and, ultimately, acceptability of Information Society Technologies by anyone, anywhere, at anytime, and through any media and device. ▶ Focuses on theoretical, methodological, and empirical research, of both technological and non-technological nature. ▶ Features papers that report on theories, methods, tools, empirical results, reviews, case studies, and best-practice examples.

Universal access in the information society

The goal of this work is to recognise phrases and sentences being spoken by a talking face, with or without the audio. Unlike previous works that have focussed on recognising a limited number of words or phrases, we tackle lip reading as an open-world problem &#x2013; unconstrained natural language sentences, and in the wild videos. Our key contributions are: (1) a Watch, Listen, Attend and Spell (WLAS) network that learns to transcribe videos of mouth motion to characters, (2) a curriculum learning strategy to accelerate training and to reduce overfitting, (3) a Lip Reading Sentences (LRS) dataset for visual speech recognition, consisting of over 100,000 natural sentences from British television. The WLAS model trained on the LRS dataset surpasses the performance of all previous work on standard lip reading benchmark datasets, often by a significant margin. This lip reading performance beats a professional lip reader on videos from BBC television, and we also demonstrate that if audio is available, then visual information helps to improve speech recognition performance.

https://openaccess.thecvf.com/content_cvpr_2017/papers/Chung_Lip_Reading_Sentences_CVPR_2017_paper.pdf

Lip Reading Sentences in the Wild

We present the first gesture recognition system implemented end-to-end on event-based hardware, using a TrueNorth neurosynaptic processor to recognize hand gestures in real-time at low power from events streamed live by a Dynamic Vision Sensor (DVS). The biologically inspired DVS transmits data only when a pixel detects a change, unlike traditional frame-based cameras which sample every pixel at a fixed frame rate. This sparse, asynchronous data representation lets event-based cameras operate at much lower power than frame-based cameras. However, much of the energy efficiency is lost if, as in previous work, the event stream is interpreted by conventional synchronous processors. Here, for the first time, we process a live DVS event stream using TrueNorth, a natively event-based processor with 1 million spiking neurons. Configured here as a convolutional neural network (CNN), the TrueNorth chip identifies the onset of a gesture with a latency of 105 ms while consuming less than 200 mW. The CNN achieves 96.5% out-of-sample accuracy on a newly collected DVS dataset (DvsGesture) comprising 11 hand gesture categories from 29 subjects under 3 illumination conditions.

/pdf/a-low-power-fully-event-based-gesture-recognition-system-5elv7phpy7.pdf

A Low Power, Fully Event-Based Gesture Recognition System

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 providing means of communication with others with the aid of computer vision. Unlike other frequent works focusing on multiple camera, depth camera, electrical glove or visual gloves, we focused on the sole use of RGB which allows everybody to communicate with a deaf individual through their personal devices. This is not a new approach but the lack of realistic large-scale data set prevented recent computer vision trends on video classification in this filed. In this paper, we propose the first large scale ASL data set that covers over 200 signers, signer independent sets, challenging and unconstrained recording conditions and a large class count of 1000 signs. We evaluate baselines from action recognition techniques on the data set. We propose I3D, known from video classifications, as a powerful and suitable architecture for sign language recognition. We also propose new pre-trained model more appropriate for sign language recognition. Finally, We estimate the effect of number of classes and number of training samples on the recognition accuracy.

MS-ASL: A Large-Scale Data Set and Benchmark for Understanding American Sign Language.

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 RWTH-PHOENIX-Weather 2014, the standard benchmark task of the field. Research in the domain of sign language recognition has progressed significantly in the last decade, reaching a point where the task attracts much more attention than ever before. This study compiles the state of the art in a concise way to help advance the field and reveal open questions. Moreover, all of this meta study's source data is made public, easing future work with it and further expansion. The analyzed papers have been manually labeled with a set of categories. The data reveals many insights, such as, among others, shifts in the field from intrusive to non-intrusive capturing, from local to global features and the lack of non-manual parameters included in medium and larger vocabulary recognition systems. Surprisingly, RWTH-PHOENIX-Weather with a vocabulary of 1080 signs represents the only resource for large vocabulary continuous sign language recognition benchmarking world wide.

Quantitative Survey of the State of the Art in Sign Language Recognition.

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 inter and intra contextual relationships between different sign articulators to be modelled within the transformer network itself, while also maintaining channel specific information. We evaluate our approach on the RWTH-PHOENIX-Weather-2014T dataset and report competitive translation performance. Importantly, we overcome the reliance on gloss annotations which underpin other state-of-the-art approaches, thereby removing future need for expensive curated datasets.

Multi-channel Transformers for Multi-articulatory Sign Language Translation

Sign language recognition is a challenging and often underestimated problem comprising multi-modal articulators (handshape, orientation, movement, upper body and face) that integrate asynchronously on multiple streams. Learning powerful statistical models in such a scenario requires much data, particularly to apply recent advances of the field. However, labeled data is a scarce resource for sign language due to the enormous cost of transcribing these unwritten languages. 
We propose the first real-life large-scale sign language data set comprising over 25,000 annotated videos, which we thoroughly evaluate with state-of-the-art methods from sign and related action recognition. Unlike the current state-of-the-art, the data set allows to investigate the generalization to unseen individuals (signer-independent test) in a realistic setting with over 200 signers. Previous work mostly deals with limited vocabulary tasks, while here, we cover a large class count of 1000 signs in challenging and unconstrained real-life recording conditions. We further propose I3D, known from video classifications, as a powerful and suitable architecture for sign language recognition, outperforming the current state-of-the-art by a large margin. The data set is publicly available to the community.

Sign languages comprise parallel aspects and use several modalities 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 investigate early combination of features, late fusion of decisions, as well as synchronous combination on the hidden Markov model state level, and asynchronous combination on the gloss level. This is done for five modalities on two publicly available benchmark databases consisting of challenging real-life data and less complex lab-data, the state-of-the-art typically focusses on. Using modality combination, the best published word error rate on the SIGNUM database (lab-data) is improved from 11.9% to 10.7% and from 55% to 41.9% on the RWTH-PHOENIX database (challenging real-life data).

/pdf/modality-combination-techniques-for-continuous-sign-language-v0wq2hpams.pdf

Oscar Koller

Papers

MS-ASL: A Large-Scale Data Set and Benchmark for Understanding American Sign Language.

Quantitative Survey of the State of the Art in Sign Language Recognition.

Multi-channel Transformers for Multi-articulatory Sign Language Translation

MS-ASL: A Large-Scale Data Set and Benchmark for Understanding American Sign Language.

Modality Combination Techniques for Continuous Sign Language Recognition