  A new label smoothing method that makes use of prior knowledge of a language
at human level, homophone, is proposed in this paper for automatic speech
recognition (ASR). Compared with its forerunners, the proposed method uses
pronunciation knowledge of homophones in a more complex way. End-to-end ASR
models that learn acoustic model and language model jointly and modelling units
of characters are necessary conditions for this method. Experiments with hybrid
CTC sequence-to-sequence model show that the new method can reduce character
error rate (CER) by 0.4% absolutely.
