  Frame stacking is broadly applied in end-to-end neural network training like
connectionist temporal classification (CTC), and it leads to more accurate
models and faster decoding. However, it is not well-suited to conventional
neural network based on context-dependent state acoustic model, if the decoder
is unchanged. In this paper, we propose a novel frame retaining method which is
applied in decoding. The system which combined frame retaining with frame
stacking could reduces the time consumption of both training and decoding. Long
short-term memory (LSTM) recurrent neural networks (RNNs) using it achieve
almost linear training speedup and reduces relative 41\% real time factor
(RTF). At the same time, recognition performance is no degradation or improves
sightly on Shenma voice search dataset in Mandarin.
