  With deep learning approaches becoming state-of-the-art in many speech (as
well as non-speech) related machine learning tasks, efforts are being taken to
delve into the neural networks which are often considered as a black box. In
this paper it is analyzed how recurrent neural network (RNNs) cope with
temporal dependencies by determining the relevant memory time span in a long
short-term memory (LSTM) cell. This is done by leaking the state variable with
a controlled lifetime and evaluating the task performance. This technique can
be used for any task to estimate the time span the LSTM exploits in that
specific scenario. The focus in this paper is on the task of separating
speakers from overlapping speech. We discern two effects: A long term effect,
probably due to speaker characterization and a short term effect, probably
exploiting phone-size formant tracks.
