  Robust and far-field speech recognition is critical to enable true hands-free
communication. In far-field conditions, signals are attenuated due to distance.
To improve robustness to loudness variation, we introduce a novel frontend
called per-channel energy normalization (PCEN). The key ingredient of PCEN is
the use of an automatic gain control based dynamic compression to replace the
widely used static (such as log or root) compression. We evaluate PCEN on the
keyword spotting task. On our large rerecorded noisy and far-field eval sets,
we show that PCEN significantly improves recognition performance. Furthermore,
we model PCEN as neural network layers and optimize high-dimensional PCEN
parameters jointly with the keyword spotting acoustic model. The trained PCEN
frontend demonstrates significant further improvements without increasing model
complexity or inference-time cost.
