中国科学院自动化研究所机构知识库

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. 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.