15 citations · 23 across the 3 of their papers we have counts for
4 papers
Speech Enhancement with Wide Residual Networks in Reverberant Environments
Jorge Llombart, Dayana Ribas, Antonio Miguel +3
This paper proposes a speech enhancement method which exploits the high potential of residual connections in a Wide Residual Network architecture. This is supported on single dimen…
Progressive Speech Enhancement with Residual Connections
Jorge Llombart, Dayana Ribas, Antonio Miguel +3
This paper studies the Speech Enhancement based on Deep Neural Networks. The proposed architecture gradually follows the signal transformation during enhancement by means of a visu…
Deep Speech Enhancement for Reverberated and Noisy Signals using Wide Residual Networks
Dayana Ribas, Jorge Llombart, Antonio Miguel +1
This paper proposes a deep speech enhancement method which exploits the high potential of residual connections in a wide neural network architecture, a topology known as Wide Resid…
Tied Hidden Factors in Neural Networks for End-to-End Speaker Recognition
Antonio Miguel, Jorge Llombart, Alfonso Ortega +1
In this paper we propose a method to model speaker and session variability and able to generate likelihood ratios using neural networks in an end-to-end phrase dependent speaker ve…