Human Transcription Quality Improvement
Jian Gao Hanbo Sun Cheng Cao Zheng Du
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.