  In the context of keyword spotting (KWS), the replacement of handcrafted
speech features by learnable features has not yielded superior KWS performance.
In this study, we demonstrate that filterbank learning outperforms handcrafted
speech features for KWS whenever the number of filterbank channels is severely
decreased. Reducing the number of channels might yield certain KWS performance
drop, but also a substantial energy consumption reduction, which is key when
deploying common always-on KWS on low-resource devices. Experimental results on
a noisy version of the Google Speech Commands Dataset show that filterbank
learning adapts to noise characteristics to provide a higher degree of
robustness to noise, especially when dropout is integrated. Thus, switching
from typically used 40-channel log-Mel features to 8-channel learned features
leads to a relative KWS accuracy loss of only 3.5% while simultaneously
achieving a 6.3x energy consumption reduction.
