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Found 4 papers, 1 papers with code
Using Rater and System Metadata to Explain Variance in the VoiceMOS Challenge 2022 Dataset
Non-reference speech quality models are important for a growing number of applications.
DNSMOS P.835: A Non-Intrusive Perceptual Objective Speech Quality Metric to Evaluate Noise Suppressors
In this work, we train an objective metric based on P. 835 human ratings that outputs 3 scores: i) speech quality (SIG), ii) background noise quality (BAK), and iii) the overall quality (OVRL) of the audio.
Interspeech 2021 Deep Noise Suppression Challenge
In this version of the challenge organized at INTERSPEECH 2021, we are expanding both our training and test datasets to accommodate full band scenarios.
DNSMOS: A Non-Intrusive Perceptual Objective Speech Quality metric to evaluate Noise Suppressors
The no-reference approaches correlate poorly with human ratings and are not widely adopted in the research community.
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