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SCOREQ: Speech Quality Assessment with Contrastive Regression - PyTorch weights

appsOther research productkeyboard_double_arrow_right Model 27 Sep 2024Publisher:Zenodo
Authors: Ragano, Alessandro;

doi: 10.5281/zenodo.13860325 , 10.5281/zenodo.15739280 , 10.5281/zenodo.13860326

SCOREQ: Speech Quality Assessment with Contrastive Regression - PyTorch weights

Abstract

Model Training Set/Domain Usage Mode Margin adapt_nr_telephone.pt/.onnx NISQA TRAIN SIM / Telephone speech No-reference Adaptive fixed_nmr_telephone.pt/.onnx NISQA TRAIN SIM / Telephone speech Non-matching reference, full-reference Constant adapt_nr_synthetic.pt/.onnx VoiceMOS 22 / Synthetic speech No-reference Adaptive fixed_nmr_synthetic.pt/.onnx VoiceMOS 22 / Synthetic speech Non-matching reference Constant

Cite as Alessandro Ragano, Jan Skoglund, and Andrew Hines (2024) SCOREQ: Speech Quality Assessment with Contrastive Regression, Advances in Neural Information Processing Systems (NeurIPS) 2024. 

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average