Supporting Collaborative Transcription of Recorded Speech with a 3D Game Interface

Luz, Saturnino; Masoodian, Masood; Rogers, Bill

doi:10.1007/978-3-642-15384-6_42

Cited by 8 publications

(7 citation statements)

References 16 publications

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“…First, alternative variants of intelligent interaction strategies which, while allowing lecturers full control over transcription quality, are better able to exploit confidence measures and visual representation (Luz et al (2008(Luz et al ( , 2010). Second, the most suitable interface design for transcription review could be determined on a case-by-case basis, perhaps as a function of WER.…”

Section: Discussionmentioning

confidence: 99%

Efficiency and usability study of innovative computer-aided transcription strategies for video lecture repositories

Miró

Silvestre-Cerdà

Civera

et al. 2015

Speech Communication

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ElsevierValor Miró, JD.; Silvestre Cerdà, JA.; Civera Saiz, J.; Turró Ribalta, C.; Juan Císcar, A. (2015). Efficiency and usability study of innovative computer-aided transcription strategies for video lecture repositories. AbstractVideo lectures are widely used in education to support and complement face-to-face lectures. However, the utility of these audiovisual assets could be further improved by adding subtitles that can be exploited to incorporate added-value functionalities such as searchability, accessibility, translatability, note-taking, and discovery of content-related videos, among others. Today, automatic subtitles are prone to error, and need to be reviewed and post-edited in order to ensure that what students see on-screen are of an acceptable quality. This work investigates different user interface design strategies for this post-editing task to discover the best way to incorporate automatic transcription technologies into large educational video repositories. Our three-phase study involved lecturers from the Universitat Politècnica de València (UPV) with videos available on the poliMedia video lecture repository, which is currently over 10,000 video objects. Simply by conventional post-editing automatic transcriptions users almost reduced to half the time that would require to generate the transcription from scratch. As expected, this study revealed that the time spent by lecturers reviewing automatic transcriptions correlated directly with the accuracy of said transcriptions. However, it is also shown that the average time required to perform each individual editing operation could be precisely derived and could be applied in the definition of a user model. In addition, the second phase of this study presents a transcription review strategy based on confidence measures (CM) and compares it to the conventional post-editing strategy. Finally, a third strategy resulting from the combination of that based on CM with massive adaptation techniques for ASR achieved to improve the transcription review efficiency in comparison with the two aforementioned strategies.

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Section: Discussionmentioning

confidence: 99%

Efficiency and usability study of innovative computer-aided transcription strategies for video lecture repositories

Miró

Silvestre-Cerdà

Civera

et al. 2015

Speech Communication

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“…While prior work has investigated offline captioning using the crowd [8,9], Legion:Scribe is the only system and worker interface for real-time online captioning [7]. Scribe extends the idea of using continuous stream of input from workers to real-time captioning, generating transcripts by combining multiple workers' partial captions into a single final caption stream.…”

Section: Related Workmentioning

confidence: 99%

Warping time for more effective real-time crowdsourcing

Lasecki

Miller

Bigham

2013

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

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In this paper, we introduce the idea of "warping time" to improve crowd performance on the difficult task of captioning speech in real-time. Prior work has shown that the crowd can collectively caption speech in real-time by merging the partial results of multiple workers. Because non-expert workers cannot keep up with natural speaking rates, the task is frustrating and prone to errors as workers buffer what they hear to type later. The TimeWarp approach automatically increases and decreases the speed of speech playback systematically across individual workers who caption only the periods played at reduced speed. Studies with 139 remote crowd workers and 24 local participants show that this approach improves median coverage (14.8%), precision (11.2%), and per-word latency (19.1%). Warping time may also help crowds outperform individuals on other difficult real-time performance tasks.

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“…The research for supporting people with hearing impairments has mainly targeted the transcription from voice to text. Two systems for offline transcription were introduced by Luz et al (2010) and Liem et al (2011). The system by Luz et al (2010) supported collaborative transcription of voice with a three-dimensional game interface.…”

Section: Related Workmentioning

confidence: 99%

“…Two systems for offline transcription were introduced by Luz et al (2010) and Liem et al (2011). The system by Luz et al (2010) supported collaborative transcription of voice with a three-dimensional game interface. The system by Liem et al (2011) proposed an iterative dual pathway structure for transcription.…”

Section: Related Workmentioning

confidence: 99%

“…Some crowdsourcing-based systems for the transcription that are to caption voice to text (Conversion 1) have been developed (Matthews et al , 2006; Luz et al , 2010; Liem et al , 2011; Lasecki et al , 2012, 2013). However, compared to spoken language, sign language has some unique properties, which makes the methods proposed in previous research not work well for the captioning of sign language.…”

Section: Introductionmentioning

confidence: 99%

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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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Supporting Collaborative Transcription of Recorded Speech with a 3D Game Interface

Cited by 8 publications

References 16 publications

Efficiency and usability study of innovative computer-aided transcription strategies for video lecture repositories

Efficiency and usability study of innovative computer-aided transcription strategies for video lecture repositories

Warping time for more effective real-time crowdsourcing

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

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