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Hybrid and end-to-end (E2E) systems have their individual advantages, withdifferent error patterns in the speech recognition results. By jointly modelingaudio and text, the E2E model performs better in matched scenarios and scaleswell with a large amount of paired audio-text training data. The modularizedhybrid model is easier for customization, and better to make use of a massiveamount of unpaired text data. This paper proposes a two-pass hybrid and E2Ecascading (HEC) framework to combine the hybrid and E2E model in order to takeadvantage of both sides, with hybrid in the first pass and E2E in the secondpass. We show that the proposed system achieves 8-10% relative word error ratereduction with respect to each individual system. More importantly, comparedwith the pure E2E system, we show the proposed system has the potential to keepthe advantages of hybrid system, e.g., customization and segmentationcapabilities. We also show the second pass E2E model in HEC is robust withrespect to the change in the first pass hybrid model.

Have best of both worlds: two-pass hybrid and E2E cascading framework for speech recognition