This is to certify that I have examined this copy of master's thesis by
Satanjeev Banerjeeand have found that it is complete and satisfactory in all respects, and that any and all revisions required by the final examining committee have been made.
We investigate whether Amazon's Mechanical Turk (MTurk) service can be used as a reliable method for transcription of spoken language data. Utterances with varying speaker demographics (native and non-native English, male and female) were posted on the MTurk marketplace together with standard transcription guidelines. Transcriptions were compared against transcriptions carefully prepared in-house through conventional (manual) means. We found that transcriptions from MTurk workers were generally quite accurate. Further, when transcripts for the same utterance produced by multiple workers were combined using the ROVER voting scheme, the accuracy of the combined transcript rivaled that observed for conventional transcription methods. We also found that accuracy is not particularly sensitive to payment amount, implying that high quality results can be obtained at a fraction of the cost and turnaround time of conventional methods.
Much work in the area of Computer Supported Cooperative Work (CSCW) has targeted the problem of supporting meetings between collaborators who are non-collocated, enabling meetings to transcend boundaries of space. In this paper, we explore the beginnings of a proposed solution for allowing meetings to transcend time as well. The need for such a solution is motivated by a user survey in which busy professionals are questioned about meetings they have either missed or forgotten the important details about after the fact. Our proposed solution allows these professionals to transcend time in a sense by revisiting a recorded meeting that has been structured for quick retrieval of sought information. Such a solution supports complete recovery of prior discussions, allowing needed information to be retrieved quickly, and thus potentially facilitating the effective continuation of discussions from the past. We evaluate the proposed solution with a formal user study in which we measure the impact of the proposed structural annotations on retrieval of information. The results of the study show that participants took significantly less time to retrieve the answers when they had access to discourse structure based annotation than in a control condition in which they had access only to unannotated video recordings (p < 0.01, effect size 0.94 standard deviations).
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.