  We address far-field speaker verification with deep neural network (DNN)
based speaker embedding extractor, where mismatch between enrollment and test
data often comes from convolutive effects (e.g. room reverberation) and noise.
To mitigate these effects, we focus on two parametric normalization methods:
per-channel energy normalization (PCEN) and parameterized cepstral mean
normalization (PCMN). Both methods contain differentiable parameters and thus
can be conveniently integrated to, and jointly optimized with the DNN using
automatic differentiation methods. We consider both fixed and trainable
(data-driven) variants of each method. We evaluate the performance on Hi-MIA, a
recent large-scale far-field speech corpus, with varied microphone and
positional settings. Our methods outperform conventional mel filterbank
features, with maximum of 33.5% and 39.5% relative improvement on equal error
rate under matched microphone and mismatched microphone conditions,
respectively.
