  In this paper, we propose an approach to quantitatively analyze impacts of
different training label errors to RNN-T based ASR models. The result shows
deletion errors are more harmful than substitution and insertion label errors
in RNN-T training data. We also examined label error impact mitigation
approaches on RNN-T and found that, though all the methods mitigate the
label-error-caused degradation to some extent, they could not remove the
performance gap between the models trained with and without the presence of
label errors. Based on the analysis results, we suggest to design data
pipelines for RNN-T with higher priority on reducing deletion label errors. We
also find that ensuring high-quality training labels remains important, despite
of the existence of the label error mitigation approaches.
