  End-to-end (E2E) models have achieved promising results on multiple speech
recognition benchmarks, and shown the potential to become the mainstream.
However, the unified structure and the E2E training hamper injecting contextual
information into them for contextual biasing. Though contextual LAS (CLAS)
gives an excellent all-neural solution, the degree of biasing to given context
information is not explicitly controllable. In this paper, we focus on
incorporating context information into the continuous integrate-and-fire (CIF)
based model that supports contextual biasing in a more controllable fashion.
Specifically, an extra context processing network is introduced to extract
contextual embeddings, integrate acoustically relevant context information and
decode the contextual output distribution, thus forming a collaborative
decoding with the decoder of the CIF-based model. Evaluated on the named entity
rich evaluation sets of HKUST/AISHELL-2, our method brings relative character
error rate (CER) reduction of 8.83%/21.13% and relative named entity character
error rate (NE-CER) reduction of 40.14%/51.50% when compared with a strong
baseline. Besides, it keeps the performance on original evaluation set without
degradation.

Computation and Language;Artificial Intelligence;Machine Learning;Sound;Audio and Speech Processing