  We previously proposed contextual spelling correction (CSC) to correct the
output of end-to-end (E2E) automatic speech recognition (ASR) models with
contextual information such as name, place, etc. Although CSC has achieved
reasonable improvement in the biasing problem, there are still two drawbacks
for further accuracy improvement. First, due to information limitation in text
only hypothesis or weak performance of ASR model on rare domains, the CSC model
may fail to correct phrases with similar pronunciation or anti-context cases
where all biasing phrases are not present in the utterance. Second, there is a
discrepancy between the training and inference of CSC. The bias list in
training is randomly selected but in inference there may be more similarity
between ground truth phrase and other phrases. To solve above limitations, in
this paper we propose an improved non-autoregressive (NAR) spelling correction
model for contextual biasing in E2E neural transducer-based ASR systems to
improve the previous CSC model from two perspectives: Firstly, we incorporate
acoustics information with an external attention as well as text hypotheses
into CSC to better distinguish target phrase from dissimilar or irrelevant
phrases. Secondly, we design a semantic aware data augmentation schema in
training phrase to reduce the mismatch between training and inference to
further boost the biasing accuracy. Experiments show that the improved method
outperforms the baseline ASR+Biasing system by as much as 20.3% relative name
recall gain and achieves stable improvement compared to the previous CSC method
over different bias list name coverage ratio.
