Sequence-to-sequence attention-based models have recently
shown very promising results on automatic speech recognition
(ASR) tasks, which integrate an acoustic, pronunciation and
language model into a single neural network. In these models,
the Transformer, a new sequence-to-sequence attentionbased
model relying entirely on self-attention without using
RNNs or convolutions, achieves a new single-model state-ofthe-
art BLEU on neural machine translation (NMT) tasks. Since
the outstanding performance of the Transformer, we extend
it to speech and concentrate on it as the basic architecture of
sequence-to-sequence attention-based model on Mandarin Chinese
ASR tasks. Furthermore, we investigate a comparison between
syllable based model and context-independent phoneme
(CI-phoneme) based model with the Transformer in Mandarin
Chinese. Additionally, a greedy cascading decoder with the
Transformer is proposed for mapping CI-phoneme sequences
and syllable sequences into word sequences. Experiments on
HKUST datasets demonstrate that syllable based model with
the Transformer performs better than CI-phoneme based counterpart,
and achieves a character error rate (CER) of 28.77%,
which is competitive to the state-of-the-art CER of 28.0% by
the joint CTC-attention based encoder-decoder network.