  There is increasing interest in the use of the LEArnable Front-end (LEAF) in
a variety of speech processing systems. However, there is a dearth of analyses
of what is actually learnt and the relative importance of training the
different components of the front-end. In this paper, we investigate this
question on keyword spotting, speech-based emotion recognition and language
identification tasks and find that the filters for spectral decomposition and
the low pass filter used to estimate spectral energy variations exhibit no
learning and the per-channel energy normalisation (PCEN) is the key component
that is learnt. Following this, we explore the potential of adapting only the
PCEN layer with a small amount of noisy data to enable it to learn appropriate
dynamic range compression that better suits the noise conditions. This in turn
enables a system trained on clean speech to work more accurately on noisy test
data as demonstrated by the experimental results reported in this paper.
