  This paper presents our latest investigations on different features for
factored language models for Code-Switching speech and their effect on
automatic speech recognition (ASR) performance. We focus on syntactic and
semantic features which can be extracted from Code-Switching text data and
integrate them into factored language models. Different possible factors, such
as words, part-of-speech tags, Brown word clusters, open class words and
clusters of open class word embeddings are explored. The experimental results
reveal that Brown word clusters, part-of-speech tags and open-class words are
the most effective at reducing the perplexity of factored language models on
the Mandarin-English Code-Switching corpus SEAME. In ASR experiments, the model
containing Brown word clusters and part-of-speech tags and the model also
including clusters of open class word embeddings yield the best mixed error
rate results. In summary, the best language model can significantly reduce the
perplexity on the SEAME evaluation set by up to 10.8% relative and the mixed
error rate by up to 3.4% relative.
