High quality transcription data is crucial for training automatic speech
recognition (ASR) systems. However, the existing industry-level data collection
pipelines are expensive to researchers, while the quality of crowdsourced
transcription is low. In this paper, we propose a reliable method to collect
speech transcriptions. We introduce two mechanisms to improve transcription
quality: confidence estimation based reprocessing at labeling stage, and
automatic word error correction at post-labeling stage. We collect and release
LibriCrowd - a large-scale crowdsourced dataset of audio transcriptions on 100
hours of English speech. Experiment shows the Transcription WER is reduced by
over 50%. We further investigate the impact of transcription error on ASR model
performance and found a strong correlation. The transcription quality
improvement provides over 10% relative WER reduction for ASR models. We release
the dataset and code to benefit the research community.

我们提出了一种可靠的方法来收集语音转录，通过在标注阶段进行置信度估计重新处理和在标注后进行自动词错误校正来提高转录质量。我们发布了 LibriCrowd—— 一个包含 100 小时英语语音的大规模众包数据集。实验结果显示，转录错误率降低了 50% 以上。我们进一步研究了转录错误对 ASR 模型性能的影响，并发现了强相关性。转录质量的提高可以使 ASR 模型的错误率下降 10% 以上。我们将数据集和代码发布给研究社区，以造福广大研究者。