How to feed variable-length of speech feature to RNN(LSTM) for Speech Recognition?
it depends on how your system built is it end-to-end training or did u use hand-engineering features such MFCC? one more note the main use of RNN's is to have a variable-length input.
I am trying to build a Speech Recognition System, which is a squence-to-sequence model. But I got confused about how to feed the extracted feature(fbank with the dimension of 40) to LSTM. As far as I have found, there are different methods to feed the data as input into LSTM. However, I have a doubt to fully understand them. I would be so thankful if someone tells me whether or not I am correct in the following cases.
Case 1:
In the convenient format [Batch_Size, Time_Step, Feature_Dim], If I select [1, None, 40], the length of each sequence(utterance) can be varied? if so, in this case I do not need to pad each sequence, am I right?
Case 2: 
If all input sequences are padded to the same length, the Batch_Size can be any value like 64, 128 and etc?
Finally, one more question, do I notice that the Time_Step in each Batch should be the same?
I would be so thankful if someone can help me to get rid of my doubts or give me some suggestions.