Different types of features, like the skipping features, the higher-order features and the crossing features can all be easily supported in TDRF LMs, and the performance is improved to varying degrees.
the TDRF using the "w+c+ws+cs+cpw" features with class number 200 performs comparable to the RNNLM in both perplexity and WER.