Cross-Corpora Convolutional Deep Neural Network Dereverberation Preprocessing for Speaker Verification and Speech Enhancement

Peter Guzewich, Stephen Zahorian, Xiao Chen, Hao Zhang

Deep neural network (DNN) dereverberation preprocessing has been shown to be a viable strategy for speech enhancement and increasing the accuracy of automatic speech recognition and automatic speaker verification. In this paper, an improved DNN technique based on convolutional neural networks is presented and compared to existing methods for speech enhancement and speaker verification in the presence of reverberation. This new technique is first shown to enhance speech quality as compared to other existing methods. Then, a more thorough set of experiments is presented that assesses cross-corpora speaker verification performance on data that contains real reverberation and noise. A discussion of the applicability and generalizability of such techniques is given.

doi: 10.21437/Interspeech.2018-2238

Cite as: Guzewich, P., Zahorian, S., Chen, X., Zhang, H. (2018) Cross-Corpora Convolutional Deep Neural Network Dereverberation Preprocessing for Speaker Verification and Speech Enhancement. Proc. Interspeech 2018, 1329-1333, doi: 10.21437/Interspeech.2018-2238

@inproceedings{guzewich18_interspeech,
  author={Peter Guzewich and Stephen Zahorian and Xiao Chen and Hao Zhang},
  title={{Cross-Corpora Convolutional Deep Neural Network Dereverberation Preprocessing for Speaker Verification and Speech Enhancement}},
  year=2018,
  booktitle={Proc. Interspeech 2018},
  pages={1329--1333},
  doi={10.21437/Interspeech.2018-2238}
}