Text correction after automatic speech recognition(ASR)is an im-portant method to improve the speech recognition system.We regard the speech error correction as a translation task—from the language of bad Chinese to the language of good Chinese.We propose a speech recognition error correction algorithm based on neural machine translation(NMT)model.The algorithm is characterized by Chinese Pinyin coding,using a multilayer convolutional en-coder-decoder with attention neural network.In the WeChat speech transcrip-tion data set we collected,our model substantially outperforms all prior neural approaches on this data set as well as the strong statistical machine translation-based systems.Our analysis shows the superiority of convolutional neural net-works in capturing the local context via attention and thereby improving the coverage in speech transcription errors.By boosting multiple modes,using data augmentation and 3-gram language model tricks,our novel algorithm makes the error rate on the test set decreased by 26.2%on average.Our results show that using a multilayer convolutional encoder-decoder with Pinyin feature is able to achieve state-of-the-art performance in text correction after speech recognition.