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.

本文提出了一种基于数据驱动的方法来解决代码切换语音识别中语音集限制、不同语言音素集和高昂的重建成本等挑战，其核心工作包括音标解码和不同选择策略。我们的实验结果表明，通过我们的方法，混杂的单词层中文英文代码切换识别的混合误差率从 29.15％降至 11.14％。