  For most of the attention-based sequence-to-sequence models, the decoder
predicts the output sequence conditioned on the entire input sequence processed
by the encoder. The asynchronous problem between the encoding and decoding
makes these models difficult to be applied for online speech recognition. In
this paper, we propose a model named synchronous transformer to address this
problem, which can predict the output sequence chunk by chunk. Once a
fixed-length chunk of the input sequence is processed by the encoder, the
decoder begins to predict symbols immediately. During training, a
forward-backward algorithm is introduced to optimize all the possible alignment
paths. Our model is evaluated on a Mandarin dataset AISHELL-1. The experiments
show that the synchronous transformer is able to perform encoding and decoding
synchronously, and achieves a character error rate of 8.91% on the test set.
