  Training speech translation (ST) models requires large and high-quality
datasets. MuST-C is one of the most widely used ST benchmark datasets. It
contains around 400 hours of speech-transcript-translation data for each of the
eight translation directions. This dataset passes several quality-control
filters during creation. However, we find that MuST-C still suffers from three
major quality issues: audio-text misalignment, inaccurate translation, and
unnecessary speaker's name. What are the impacts of these data quality issues
for model development and evaluation? In this paper, we propose an automatic
method to fix or filter the above quality issues, using English-German (En-De)
translation as an example. Our experiments show that ST models perform better
on clean test sets, and the rank of proposed models remains consistent across
different test sets. Besides, simply removing misaligned data points from the
training set does not lead to a better ST model.
