Deep neural networks for cochannel speaker identification

Xiaojia Zhao; Yuxuan Wang; DeLiang Wang

ICASSP 2015accepted0 citations

Deep neural networks for cochannel speaker identification

Abstract

Speaker identification (SID) in cochannel speech, where two speakers are talking simultaneously over a single recording channel, is a challenging problem. Previous studies address this problem in the anechoic environment under the Gaussian mixture model (GMM) framework. On the other hand, cochannel SID in reverberant conditions has not been addressed. This paper studies cochannel SID in both anechoic and reverberant conditions. We explore deep neural networks (DNNs) for cochannel SID and propose a DNN-based recognition system. Evaluation results demonstrate the proposed DNN-based system outperforms the two state-of-the-art cochannel SID systems in both anechoic and reverberant conditions and various target-to-interferer ratios.

BibTeX

@inproceedings{icassp2015_deepneuralnetwor,
  title = {Deep neural networks for cochannel speaker identification},
  author = {Xiaojia Zhao and Yuxuan Wang and DeLiang Wang},
  booktitle = {ICASSP 2015},
  year = {2015}
}