Investigation of Transformer Based Spelling Correction Model for CTC-Based End-to-End Mandarin Speech Recognition

Connectionist Temporal Classification (CTC) based end-to-end speech recognition system usually need to incorporate an external language model by using WFST-based decoding in order to achieve promising results. This is more essential to Mandarin speech recognition since it owns a special phenomenon, namely homophone, which causes a lot of substitution errors. The linguistic information introduced by language model is somehow helpful to distinguish these substitution errors. In this work, we propose a transformer based spelling correction model to automatically correct errors, especially the substitution errors, made by CTC-based Mandarin speech recognition system. Specifically, we investigate to use the recognition results generated by CTC-based systems as input and the ground-truth transcriptions as output to train a transformer with encoder-decoder architecture, which is much similar to machine translation. Experimental results in a 20,000 hours Mandarin speech recognition task show that the proposed spelling correction model can achieve a CER of 3.41%, which results in 22.9% and 53.2% relative improvement compared to the baseline CTC-based systems decoded with and without language model, respectively.