  In this paper, we present a reverberation removal approach for speaker
verification, utilizing dual-label deep neural networks (DNNs). The networks
perform feature mapping between the spectral features of reverberant and clean
speech. Long short term memory recurrent neural networks (LSTMs) are trained to
map corrupted Mel filterbank (MFB) features to two sets of labels: i) the clean
MFB features, and ii) either estimated pitch tracks or the fast Fourier
transform (FFT) spectrogram of clean speech. The performance of reverberation
removal is evaluated by equal error rates (EERs) of speaker verification
experiments.
