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DNSMOS P.835: A Non-Intrusive Perceptual Objective Speech Quality Metric to Evaluate Noise Suppressors

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arxiv 2110.01763 v4 pith:UOVESPCD submitted 2021-10-05 eess.AS cs.SD

classification eess.AScs.SD
keywords qualityscoresspeechhumanmetricnoisesubjectivednsmos
verification ladder T0 review T1 audit T2 compute T3 formal

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Human subjective evaluation is the gold standard to evaluate speech quality optimized for human perception. Perceptual objective metrics serve as a proxy for subjective scores. We have recently developed a non-intrusive speech quality metric called Deep Noise Suppression Mean Opinion Score (DNSMOS) using the scores from ITU-T Rec. P.808 subjective evaluation. The P.808 scores reflect the overall quality of the audio clip. ITU-T Rec. P.835 subjective evaluation framework gives the standalone quality scores of speech and background noise in addition to the overall quality. 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. The developed metric is highly correlated with human ratings, with a Pearson's Correlation Coefficient (PCC)=0.94 for SIG and PCC=0.98 for BAK and OVRL. This is the first non-intrusive P.835 predictor we are aware of. DNSMOS P.835 is made publicly available as an Azure service.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. UniFlow: Unifying Speech Front-End Tasks via Continuous Generative Modeling

    eess.AS 2025-08 conditional novelty 6.0 of 10

    UniFlow unifies four speech front-end tasks in one continuous-latent generative model with task-ID conditioning and reports competitive, but not uniformly superior, benchmark scores.

  2. Conditional Latent Diffusion-Based Speech Enhancement Via Dual Context Learning

    cs.SD 2025-01 conditional novelty 6.0 of 10

    A latent diffusion model for speech enhancement that also learns to generate background noise, improving quality and generalization to unseen noise with fewer diffusion steps.

  3. Improved in-car sound pick-up using multichannel Wiener filter

    eess.AS 2025-06 conditional novelty 5.0 of 10

    In a simulated car, a multichannel Wiener filter separates two speakers and suppresses background noise, outperforming the simple sum of two microphone signals.

  4. DPDFNet: Boosting DeepFilterNet2 via Dual-Path RNN

    cs.SD 2025-12 conditional novelty 4.0 of 10

    DPDFNet inserts dual-path RNN blocks into DeepFilterNet2's encoder, adds an over-attenuation loss and long-context fine-tuning, and reports superior causal speech enhancement on a 12-language low-SNR test set.

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