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We present a state-of-the-art end-to-end Automatic Speech Recognition (ASR)model. We learn to listen and write characters with a joint ConnectionistTemporal Classification (CTC) and attention-based encoder-decoder network. Theencoder is a deep Convolutional Neural Network (CNN) based on the VGG network.The CTC network sits on top of the encoder and is jointly trained with theattention-based decoder. During the beam search process, we combine the CTCpredictions, the attention-based decoder predictions and a separately trainedLSTM language model. We achieve a 5-10\% error reduction compared to priorsystems on spontaneous Japanese and Chinese speech, and our end-to-end modelbeats out traditional hybrid ASR systems.

Advances in Joint CTC-Attention based End-to-End Speech Recognition with a Deep CNN Encoder and RNN-LM