INTERSPEECH 2014
15th Annual Conference of the International Speech Communication Association

Singapore
September 14-18, 2014

Binaural Deep Neural Network Classification for Reverberant Speech Segregation

Yi Jiang (1), DeLiang Wang (2), RunSheng Liu (1)

(1) Tsinghua University, China
(2) Ohio State University, USA

While human listening is robust in complex auditory scenes, current speech segregation algorithms do not perform well in noisy and reverberant environments. This paper addresses the robustness in binaural speech segregation by employing binary classification based on deep neural networks (DNNs). We systematically examine DNN generalization to untrained configurations. Evaluations and comparisons show that DNN based binaural classification produces superior segregation performance in a variety of multisource and reverberant conditions.

Full Paper

Bibliographic reference.  Jiang, Yi / Wang, DeLiang / Liu, RunSheng (2014): "Binaural deep neural network classification for reverberant speech segregation", In INTERSPEECH-2014, 2400-2404.