  In this paper, a new speech feature fusion method is proposed for speaker
recognition on the basis of the cross gate parallel convolutional neural
network (CG-PCNN). The Mel filter bank features (MFBFs) of different frequency
resolutions can be extracted from each speech frame of a speaker's speech by
several Mel filter banks, where the numbers of the triangular filters in the
Mel filter banks are different. Due to the frequency resolutions of these MFBFs
are different, there are some complementaries for these MFBFs. The CG-PCNN is
utilized to extract the deep features from these MFBFs, which applies a cross
gate mechanism to capture the complementaries for improving the performance of
the speaker recognition system. Then, the fusion feature can be obtained by
concatenating these deep features for speaker recognition. The experimental
results show that the speaker recognition system with the proposed speech
feature fusion method is effective, and marginally outperforms the existing
state-of-the-art systems.
