255 citations · 1.2k across the 38 of their papers we have counts for
6 papers · 2 filters
Differentiable Consistency Constraints for Improved Deep Speech Enhancement
Scott Wisdom, John R. Hershey, Kevin Wilson +4
In recent years, deep networks have led to dramatic improvements in speech enhancement by framing it as a data-driven pattern recognition problem. In many modern enhancement system…
Exploring Tradeoffs in Models for Low-latency Speech Enhancement
Kevin Wilson, Michael Chinen, Jeremy Thorpe +5
We explore a variety of neural networks configurations for one- and two-channel spectrogram-mask-based speech enhancement. Our best model improves on previous state-of-the-art perf…
SDR - half-baked or well done?
Jonathan Le Roux, Scott Wisdom, Hakan Erdogan +1
In speech enhancement and source separation, signal-to-noise ratio is a ubiquitous objective measure of denoising/separation quality. A decade ago, the BSS_eval toolkit was develop…
Phasebook and Friends: Leveraging Discrete Representations for Source Separation
Jonathan Le Roux, Gordon Wichern, Shinji Watanabe +2
Deep learning based speech enhancement and source separation systems have recently reached unprecedented levels of quality, to the point that performance is reaching a new ceiling.…
A Purely End-to-end System for Multi-speaker Speech Recognition
Hiroshi Seki, Takaaki Hori, Shinji Watanabe +2
Recently, there has been growing interest in multi-speaker speech recognition, where the utterances of multiple speakers are recognized from their mixture. Promising techniques hav…
End-to-End Speech Separation with Unfolded Iterative Phase Reconstruction
Zhong-Qiu Wang, Jonathan Le Roux, DeLiang Wang +1
This paper proposes an end-to-end approach for single-channel speaker-independent multi-speaker speech separation, where time-frequency (T-F) masking, the short-time Fourier transf…