  Code-Switching refers to the phenomenon of switching languages within a
sentence or discourse. However, limited code-switching , different language
phoneme-sets and high rebuilding costs throw a challenge to make the
specialized acoustic model for code-switching speech recognition. In this
paper, we make use of limited code-switching data as driving materials and
explore a shortcut to quickly develop intra-sentential code-switching
recognition skill on the commissioned native language acoustic model, where we
propose a data-driven method to make the seed lexicon which is used to train
grapheme-to-phoneme model to predict mapping pronunciations for foreign
language word in code-switching sentences. The core work of the data-driven
technology in this paper consists of a phonetic decoding method and different
selection methods. And for imbalanced word-level driving materials problem, we
have an internal assistance inspiration that learning the good pronunciation
rules in the words that possess sufficient materials using the
grapheme-to-phoneme model to help the scarce. Our experiments show that the
Mixed Error Rate in intra-sentential Chinese-English code-switching recognition
reduced from 29.15\%, acquired on the pure Chinese recognizer, to 12.13\% by
adding foreign language words' pronunciation through our data-driven approach,
and finally get the best result 11.14\% with the combination of different
selection methods and internal assistance tactic.
