InterSpeech 2021

End-to-End Spelling Correction Conditioned on Acoustic Feature for Code-switching Speech Recognition
(3 minutes introduction)

Shuai Zhang (UCAS, China), Jiangyan Yi (CAS, China), Zhengkun Tian (UCAS, China), Ye Bai (UCAS, China), Jianhua Tao (UCAS, China), Xuefei Liu (CAS, China), Zhengqi Wen (CAS, China)
In this work, we propose a new end-to-end (E2E) spelling correction 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 diversity 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 alleviate 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 significantly increasing the computational cost. The experimental results on ASRU 2019 Mandarin-English Code-switching Challenge data set show that the proposed method achieves 11.14% relative error rate reduction compared with baseline.