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.
Towards robust speech representa- tion learning for thousands of languages
2 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
AASIST3 integrates KANs and other modifications into AASIST to improve deepfake speech detection, achieving minDCF of 0.5357 (closed) and 0.1414 (open) on ASVspoof 2024.
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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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AASIST3: KAN-Enhanced AASIST Speech Deepfake Detection using SSL Features and Additional Regularization for the ASVspoof 2024 Challenge
AASIST3 integrates KANs and other modifications into AASIST to improve deepfake speech detection, achieving minDCF of 0.5357 (closed) and 0.1414 (open) on ASVspoof 2024.