Toward better crowdsourced transcription: Transcription of a year of the Let´s Go Bus Information System data

Transcription is typically a long and expensive process. In the last year, crowdsourcing through Amazon Mechanical Turk (MTurk) has emerged as a way to transcribe large amounts of speech. This paper presents a two-stage approach for the use of MTurk to transcribe one year of Let´s Go Bus Information System data, corresponding to 156.74 hours (257,658 short utterances). This data was made available for the Spoken Dialog Challenge 2010. While others have used a one stage approach, asking workers to label, for example, words and noises in the same pass, the present approach is closer to what expert transcribers do, dividing one complicated task into several less complicated ones with the goal of obtaining a higher quality transcript. The two stage approach shows better results in terms of agreement with experts and the quality of acoustic modeling. When “gold-standard” quality control is used, the quality of the transcripts comes close to NIST published expert agreement, although the cost doubles.