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Walter S. Lasecki


Publications by Walter S. Lasecki (bibliography)

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Kushalnagar, Raja S., Lasecki, Walter S. and Bigham, Jeffrey P. (2012): A readability evaluation of real-time crowd captions in the classroom. In: Fourteenth Annual ACM SIGACCESS Conference on Assistive Technologies 2012. pp. 71-78. http://dx.doi.org/10.1145/2384916.2384930

Deaf and hard of hearing individuals need accommodations that transform aural to visual information, such as captions that are generated in real-time to enhance their access to spoken information in lectures and other live events. The captions produced by professional captionists work well in general events such as community or legal meetings, but is often unsatisfactory in specialized content events such as higher education classrooms. In addition, it is hard to hire professional captionists, especially those that have experience in specialized content areas, as they are scarce and expensive. The captions produced by commercial automatic speech recognition (ASR) software are far cheaper, but is often perceived as unreadable due to ASR's sensitivity to accents, background noise and slow response time. We ran a study to evaluate the readability of captions generated by a new crowd captioning approach versus professional captionists and ASR. In this approach, captions are typed by classmates into a system that aligns and merges the multiple incomplete caption streams into a single, comprehensive real-time transcript. Our study asked 48 deaf and hearing readers to evaluate transcripts produced by a professional captionist, ASR and crowd captioning software respectively and found the readers preferred crowd captions over professional captions and ASR.

© All rights reserved Kushalnagar et al. and/or ACM Press

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Lasecki, Walter S. and Bigham, Jeffrey P. (2012): Online quality control for real-time crowd captioning. In: Fourteenth Annual ACM SIGACCESS Conference on Assistive Technologies 2012. pp. 143-150. http://dx.doi.org/10.1145/2384916.2384942

Approaches for real-time captioning of speech are either expensive (professional stenographers) or error-prone (automatic speech recognition). As an alternative approach, we have been exploring whether groups of non-experts can collectively caption speech in real-time. In this approach, each worker types as much as they can and the partial captions are merged together in real-time automatically. This approach works best when partial captions are correct and received within a few seconds of when they were spoken, but these assumptions break down when engaging workers on-demand from existing sources of crowd work like Amazon's Mechanical Turk. In this paper, we present methods for quickly identifying workers who are producing good partial captions and estimating the quality of their input. We evaluate these methods in experiments run on Mechanical Turk in which a total of 42 workers captioned 20 minutes of audio. The methods introduced in this paper were able to raise overall accuracy from 57.8% to 81.22% while keeping coverage of the ground truth signal nearly unchanged.

© All rights reserved Lasecki and Bigham and/or ACM Press

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Lasecki, Walter S., Murray, Kyle I., White, Samuel, Miller, Robert C. and Bigham, Jeffrey P. (2011): Real-time crowd control of existing interfaces. In: Proceedings of the 2011 ACM Symposium on User Interface Software and Technology 2011. pp. 23-32. http://dx.doi.org/10.1145/2047196.2047200

Crowdsourcing has been shown to be an effective approach for solving difficult problems, but current crowdsourcing systems suffer two main limitations: (i) tasks must be repackaged for proper display to crowd workers, which generally requires substantial one-off programming effort and support infrastructure, and (ii) crowd workers generally lack a tight feedback loop with their task. In this paper, we introduce Legion, a system that allows end users to easily capture existing GUIs and outsource them for collaborative, real-time control by the crowd. We present mediation strategies for integrating the input of multiple crowd workers in real-time, evaluate these mediation strategies across several applications, and further validate Legion by exploring the space of novel applications that it enables.

© All rights reserved Lasecki et al. and/or ACM Press

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