RNN as posterior probability estimation in speech recognition with HTK
It is very huge effort to build RNN-HMM system with HTK. It is easier to use more flexible toolkits like Kaldi, they have RNN-HMM already implemented.

HTK in latest version 3.5 beta already has support for DNN (not RNN but still a neural network), you can grep for HYBRIDHS in sources. For details on DNN-HMM it is better to read HTKBook 3.5 section 3.12 DNN-HMM Systems and Chapter 14 Artificial Neural Networks

You need to modify all places in HModel.c file to introduce your own type of the model. It is not a separate step you could introduce, you have to modify complex interaction between acoustic model and HMM search code.

It is also not straightforward to embed Python code into HTK C code, you might dump RNN scores in a file and read them from C, otherwise, you will have to reimplement your RNN code in C.