Toward better crowdsourced transcription: Transcription of a year of the Let's Go Bus Information System data
Toward better crowdsourced transcription: Transcription of a year of the Let's Go Bus Information System data
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DOI：
10.1109/slt.2010.5700870
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发表时间：
2010-12
期刊：
影响因子：
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通讯作者：
Gabriel Parent;M. Eskénazi
中科院分区：
文献类型：
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作者：
Gabriel Parent;M. Eskénazi
转录通常是一个漫长而昂贵的过程。在过去的一年里，通过亚马逊土耳其机器人（MTurk）的众包已经成为一种转录大量语音的方式。本文提出了一个两阶段的方法，使用MTurk转录一年的让我们去巴士信息系统的数据，对应于156.74小时（257,658短话语）。这些数据是为2010年口语对话挑战赛提供的[1]1。虽然其他人使用了一个阶段的方法，要求工人标记，例如，在同一遍中的单词和噪音，本方法更接近专家转录员所做的，将一个复杂的任务分成几个不太复杂的任务，目的是获得更高质量的转录。两阶段方法在与专家的一致性和声学建模的质量方面显示出更好的结果。当使用“黄金标准”质量控制时，转录本的质量接近NIST公布的专家协议，尽管成本增加了一倍。
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 [1]1. 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.