  The choice of modeling units is critical to automatic speech recognition
(ASR) tasks. Conventional ASR systems typically choose context-dependent states
(CD-states) or context-dependent phonemes (CD-phonemes) as their modeling
units. However, it has been challenged by sequence-to-sequence attention-based
models, which integrate an acoustic, pronunciation and language model into a
single neural network. On English ASR tasks, previous attempts have already
shown that the modeling unit of graphemes can outperform that of phonemes by
sequence-to-sequence attention-based model.
  In this paper, we are concerned with modeling units on Mandarin Chinese ASR
tasks using sequence-to-sequence attention-based models with the Transformer.
Five modeling units are explored including context-independent phonemes
(CI-phonemes), syllables, words, sub-words and characters. Experiments on HKUST
datasets demonstrate that the lexicon free modeling units can outperform
lexicon related modeling units in terms of character error rate (CER). Among
five modeling units, character based model performs best and establishes a new
state-of-the-art CER of $26.64\%$ on HKUST datasets without a hand-designed
lexicon and an extra language model integration, which corresponds to a $4.8\%$
relative improvement over the existing best CER of $28.0\%$ by the joint
CTC-attention based encoder-decoder network.
