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arXiv · 1811.07030

Exploring Tradeoffs in Models for Low-latency Speech Enhancement

Kevin Wilson·Michael Chinen·Jeremy Thorpe·Brian Patton·John Hershey·Rif A. Saurous·Jan Skoglund·Richard F. Lyon
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Abstract

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 performance on the CHiME2 speech enhancement task by 0.4 decibels in signal-to-distortion ratio (SDR). We examine trade-offs such as non-causal look-ahead, computation, and parameter count versus enhancement performance and find that zero-look-ahead models can achieve, on average, within 0.03 dB SDR of our best bidirectional model. Further, we find that 200 milliseconds of look-ahead is sufficient to achieve equivalent performance to our best bidirectional model.

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Kevin Wilson, Michael Chinen, Jeremy Thorpe, Brian Patton, John Hershey, Rif A. Saurous, Jan Skoglund, Richard F. Lyon. 2018-11-16. Exploring Tradeoffs in Models for Low-latency Speech Enhancement. https://arxiv.org/abs/1811.07030

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