Recently HMM-RNN hybrid system has been proved to be successful in speech recognition

But using HMM to dealing these tasks would need inputs and outputs to be pre-aligned

so the training process works effectively

on the other hand

when dividing signal into frames

each frames nearby will have a same part overlapped

since the calculation of RNN is context-dependent

the overlapped part increases the training time. This paper combines CTC with RNN instead of HMM

and remove the overlapped part during framing modeling TIMIT dataset on phone recognition tasks. The experiments show that CTC-BLSTM performs better than HMM-BLSTM on phone recognition

and removing the overlapped part of frames can make system more efficient and ensure the accuracy at a certain degree.