We describe a novel approach to collecting orthographically transcribed continuous speech data through the use of an online educational game called Voice Scatter, in which players study flashcards by using speech to match terms with their definitions. We analyze a corpus of 30,938 utterances, totaling 27.63 hours of speech, collected during the first 22 days that Voice Scatter was publicly available. Though each individual game covers only a small vocabulary, in aggregate speech recognition hypotheses in the corpus contain 21,758 distinct words. We show that Amazon Mechanical Turk can be used to orthographically transcribe utterances in the corpus quickly and cheaply, with near-expert accuracy. Moreover, we present a filtering technique that automatically identifies a sub-corpus of 39% of the data for which recognition hypotheses can be considered human-quality transcripts. We demonstrate the usefulness of such self-transcribed data for acoustic model adaptation.