  Recently, excellent progress has been made in speech recognition. However,
pure data-driven approaches have struggled to solve the problem in
domain-mismatch and long-tailed data. Considering that knowledge-driven
approaches can help data-driven approaches alleviate their flaws, we introduce
sememe-based semantic knowledge information to speech recognition (SememeASR).
Sememe, according to the linguistic definition, is the minimum semantic unit in
a language and is able to represent the implicit semantic information behind
each word very well. Our experiments show that the introduction of sememe
information can improve the effectiveness of speech recognition. In addition,
our further experiments show that sememe knowledge can improve the model's
recognition of long-tailed data and enhance the model's domain generalization
ability.
