Real-time captioning of sign language by groups of deaf and hard-of-hearing people

Zhang, Jianwei; Shiraishi, Yoshiaki; Kumai, Katsumi; Morishima, Atsuyuki

doi:10.1145/3011141.3011143

Cited by 3 publications

(2 citation statements)

References 20 publications

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“…In addition, some important issues for the captioning of sign language need be resolved, such as how many workers and groups are required and whether a practical level of crowdsourced real-time captioning by DHH people can be really achieved. We explore these issues and enhance the experimental content published in our previous work (Zhang et al , 2016).…”

Section: Introductionmentioning

confidence: 92%

Crowdsourced real-time captioning of sign language by deaf and hard-of-hearing people

Shiraishi

Zhang

Wakatsuki

et al. 2017

IJPCC

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Purpose The purpose of this paper is to explore the issues on how to achieve crowdsourced real-time captioning of sign language by deaf and hard-of-hearing (DHH) people, such that how a system structure should be designed, how a continuous task of sign language captioning should be divided into microtasks and how many DHH people are required to maintain a high-quality real-time captioning. Design/methodology/approach The authors first propose a system structure, including the new design of worker roles, task division and task assignment. Then, based on an implemented prototype, the authors analyze the necessary setting for achieving a crowdsourced real-time captioning of sign language, test the feasibility of the proposed system and explore its robustness and improvability through four experiments. Findings The results of Experiment 1 have revealed the optimal method for task division, the necessary minimum number of groups and the necessary minimum number of workers in a group. The results of Experiment 2 have verified the feasibility of the crowdsourced real-time captioning of sign language by DHH people. The results of Experiment 3 and Experiment 4 have shown the robustness and improvability of the captioning system. Originality/value Although some crowdsourcing-based systems have been developed for the captioning of voice to text, the authors intend to resolve the issues on the captioning of sign language to text, for which the existing approaches do not work well due to the unique properties of sign language. Moreover, DHH people are generally considered as the ones who receive support from others, but our proposal helps them become the ones who offer support to others.

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Section: Introductionmentioning

confidence: 92%

Crowdsourced real-time captioning of sign language by deaf and hard-of-hearing people

Shiraishi

Zhang

Wakatsuki

et al. 2017

IJPCC

Self Cite

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“…The final annotation quality can be significantly affected by the performance of their workers. Previous work [36] suggests that the overall quality can be increased by assigning workers with different quality levels, while keeping the average worker quality of each task as high as possible. Inspired by the finding in skill-and-stress-aware assignment [19], the Multiqueue Assignment Strategy divides workers into three groups (low, medium and high) by ranking them according to their quality score, and equally splitting the rank in three groups.…”

Section: Worker Quality-aware Task Assignmentmentioning

confidence: 99%

Crowd-Mapping Urban Objects from Street-Level Imagery

Qiu

Psyllidis

Bozzon

et al. 2019

The World Wide Web Conference

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Knowledge about the organization of the main physical elements (e.g. streets) and objects (e.g. trees) that structure cities is important in the maintenance of city infrastructure and the planning of future urban interventions. In this paper, a novel approach to crowd-mapping urban objects is proposed. Our method capitalizes on strategies for generating crowdsourced object annotations from street-level imagery, in combination with object density and geo-location estimation techniques to enable the enumeration and geo-tagging of urban objects. To address both the coverage and precision of the mapped objects within budget constraints, we design a scheduling strategy for micro-task prioritization, aggregation, and assignment to crowd workers. We experimentally demonstrate the feasibility of our approach through a use case pertaining to the mapping of street trees in New York City and Amsterdam. We show that anonymous crowds can achieve high recall (up to 80%) and precision (up to 68%), with geo-location precision of approximately 3m. We also show that similar performance could be achieved at city scale, possibly with stringent budget constraints. CCS CONCEPTS • Information systems → Geographic information systems; Crowdsourcing; Web interfaces; • Human-centered computing → Collaborative and social computing systems and tools.

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Video captioning – a survey

Vaishnavi,

Narmatha

2024

Multimed Tools Appl

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Real-time captioning of sign language by groups of deaf and hard-of-hearing people

Cited by 3 publications

References 20 publications

Crowdsourced real-time captioning of sign language by deaf and hard-of-hearing people

Crowdsourced real-time captioning of sign language by deaf and hard-of-hearing people

Crowd-Mapping Urban Objects from Street-Level Imagery

Video captioning – a survey

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