144 citations · 165 across the 14 of their papers we have counts for
4 papers · 1 filter
A Perceptually-Motivated Approach for Low-Complexity, Real-Time Enhancement of Fullband Speech
Jean-Marc Valin, Umut Isik, Neerad Phansalkar +3
Over the past few years, speech enhancement methods based on deep learning have greatly surpassed traditional methods based on spectral subtraction and spectral estimation. Many of…
PoCoNet: Better Speech Enhancement with Frequency-Positional Embeddings, Semi-Supervised Conversational Data, and Biased Loss
Umut Isik, Ritwik Giri, Neerad Phansalkar +3
Neural network applications generally benefit from larger-sized models, but for current speech enhancement models, larger scale networks often suffer from decreased robustness to t…
From Speech-to-Speech Translation to Automatic Dubbing
Marcello Federico, Robert Enyedi, Roberto Barra-Chicote +4
We present enhancements to a speech-to-speech translation pipeline in order to perform automatic dubbing. Our architecture features neural machine translation generating output of…
Channel-Attention Dense U-Net for Multichannel Speech Enhancement
Bahareh Tolooshams, Ritwik Giri, Andrew H. Song +2
Supervised deep learning has gained significant attention for speech enhancement recently. The state-of-the-art deep learning methods perform the task by learning a ratio/binary ma…