DNSMOS-C: Improving End-to-end Speech Quality Models via Contrastive Learning
Abstract
We introduce DNSMOS-C, a compact end-to-end speech quality assessment model that extends the DNSMOS Pro framework by integrating a MOS-guided triplet-based contrastive loss. Applied directly to the intermediate embeddings, this contrastive supervision encourages the latent space to be better organized with respect to perceptual quality while preserving the simplicity and efficiency of DNSMOS Pro. Unlike prior methods that depend on large pre-trained self-supervised learning (SSL) encoders and multi-stage training, DNSMOS-C jointly learns speech representations and MOS regression within a single, unified framework. Experiments on multiple datasets show that DNSMOS-C consistently improves correlation metrics over DNSMOS Pro and achieves better generalization on challenging out-of-domain test sets. Furthermore, latent space analyses indicate that our approach learns representations that exhibit an emergent low-dimensional quality ordering, which enhances interpretability and improves training stability. These findings demonstrate that MOS-guided contrastive learning enables more robust and accurate quality predictions without incurring additional computational overhead.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
DNSMOS-C improves the robustness and generalization of lightweight speech quality models by integrating MOS-guided contrastive learning into the DNSMOS Pro framework, offering a practical balance between performance, efficiency, and training stability for real-world deployment.
Methodology
The paper proposes DNSMOS-C, a modification of the existing DNSMOS Pro architecture. The core methodological contribution is the integration of a MOS-guided triplet-based contrastive loss (adapted from SCOREQ) into the training objective of a compact, end-to-end convolutional model. The authors argue that this encourages the latent space to be organized by perceptual quality rather than specific distortion types. While the application of contrastive learning to speech quality is not entirely new (SCOREQ did this for SSL features), applying it directly to the intermediate embeddings of a lightweight, end-to-end CNN without pre-trained SSL encoders is a valid and pragmatic engineering contribution. The approach is technically sound but relies heavily on adapting existing loss functions rather than proposing a novel architectural primitive or theoretical framework. The integration is straightforward: adding a weighted contrastive term to the Gaussian Negative Log-Likelihood (GNLL) loss.
Results & evaluation
The experimental evaluation is comprehensive in terms of dataset variety, covering synthetic (BVCC), simulated (NISQA, Tencent), and real-world (TCD-VoIP, ESC50) data. The results show consistent improvements in correlation metrics (LCC, SRCC) over the DNSMOS Pro baseline, particularly in out-of-domain generalization scenarios. The latent space analysis using PCA and clustering provides qualitative support for the claim that the model learns a "quality manifold." The inclusion of standard deviation over 10 runs adds credibility to the stability claims. However, the performance gains, while consistent, are modest in absolute terms (e.g., LCC improvements of ~0.01-0.02 on some splits). The trade-off analysis regarding distortion clustering vs. quality ordering is insightful but highlights a limitation in interpretability for specific artifact types.
Reproducibility
The paper provides significant detail on the methodology, including hyperparameters (learning rate, epochs, margin), data preprocessing steps (16kHz, 10s padding, log-magnitude spectrograms), and the specific loss formulations. The authors explicitly state that code and checkpoints will be available on GitHub, which significantly enhances reproducibility. The use of standard datasets and clear evaluation metrics allows for direct comparison with prior work.
Limitations
The primary limitation is the incremental nature of the novelty; it adapts a known technique (contrastive regression) to a known architecture (DNSMOS Pro). The performance gains, while statistically significant in correlation, may not be transformative for all applications. The latent space analysis shows a degradation in the ability to separate specific distortion types, which might be a drawback for diagnostic applications where identifying the *cause* of poor quality is as important as the *score*. Furthermore, the model is still limited by the capacity of a small CNN compared to larger SSL-based models, though this is a trade-off for efficiency.
Broader impact
This work contributes to the field of automatic speech quality assessment, a critical component for VoIP, streaming services, and generative speech models. By providing a more robust, efficient, and generalizable model, it facilitates the deployment of high-quality monitoring tools in resource-constrained environments. The emphasis on generalization to unseen domains addresses a key pain point in the industry. DNSMOS-C improves the robustness and generalization of lightweight speech quality models by integrating MOS-guided contrastive learning into the DNSMOS Pro framework, offering a practical balance between performance, efficiency, and training stability for real-world deployment.
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