In this work, we propose a new end-to-end (E2E) spelling cor-
rection method for post-processing of code-switching automatic
speech recognition (ASR). Existing E2E spelling correction
models take the hypotheses of ASR as inputs and annotated text
as the targets. Due to the powerful modeling capabilities of the
E2E model, the training of the correction system is extremely
prone to over-fitting. It usually requires sufficient data diver-
sity for reliable training. Therefore, it is difficult to apply the
E2E correction models to the code-switching ASR task because
of the data shortage. In this paper, we introduce the acoustic
features into the spelling correction model. Our method can al-
leviate the problem of over-fitting and has better performance.
Meanwhile, because the acoustic features are encode-free, our
proposed model can be applied to the ASR model without sig-
nificantly increasing the computational cost. The experimental
results on ASRU 2019 Mandarin-English Code-switching Chal-
lenge data set show that the proposed method achieves 11.14%
relative error rate reduction compared with baseline