SenSE adds language-model semantic guidance to flow-matching generative speech enhancement via a dual-path masked conditioning strategy and reports SOTA results on distorted speech.
The in- terspeech 2020 deep noise suppression challenge: Datasets, subjective testing framework, and challenge results.arXiv preprint arXiv:2005.13981
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Fast-ULCNet matches original ULCNet speech enhancement quality while cutting model size by more than half and latency by 34% via FastGRNN replacement and a state-drift filter.
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SenSE: Semantic-Aware High-Fidelity Universal Speech Enhancement
SenSE adds language-model semantic guidance to flow-matching generative speech enhancement via a dual-path masked conditioning strategy and reports SOTA results on distorted speech.
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Fast-ULCNet: A fast and ultra low complexity network for single-channel speech enhancement
Fast-ULCNet matches original ULCNet speech enhancement quality while cutting model size by more than half and latency by 34% via FastGRNN replacement and a state-drift filter.