Improving Speaker Verification in Reverberant Environments

Speaker verification technology has been successfully adopted and integrated into many applications. However, most of these applications require a microphone located near the talker. For the case of distant microphones, speech signals are corrupted by reverberations caused by the large speaker to microphone distance. In this paper, we first introduce a new feature set that gives more details in the frequency dimension in the 2-D time-frequency space used to represent speech. These features are computed using two sets of basis vectors, both of which are applied directly to the amplitude compressed FFT spectrum. One set of basis vectors accounts for the spectral envelope while the second set accounts for pitch. Those features are used to train a Convolutional Neural Network (CNN), with the goal of reducing the negative effects of reverberation. The proposed frontend is shown to be robust for speaker verification in reverberant environments.