  Hybrid and end-to-end (E2E) systems have their individual advantages, with
different error patterns in the speech recognition results. By jointly modeling
audio and text, the E2E model performs better in matched scenarios and scales
well with a large amount of paired audio-text training data. The modularized
hybrid model is easier for customization, and better to make use of a massive
amount of unpaired text data. This paper proposes a two-pass hybrid and E2E
cascading (HEC) framework to combine the hybrid and E2E model in order to take
advantage of both sides, with hybrid in the first pass and E2E in the second
pass. We show that the proposed system achieves 8-10% relative word error rate
reduction with respect to each individual system. More importantly, compared
with the pure E2E system, we show the proposed system has the potential to keep
the advantages of hybrid system, e.g., customization and segmentation
capabilities. We also show the second pass E2E model in HEC is robust with
respect to the change in the first pass hybrid model.
