Summary of Uncertainty-aware Mean Opinion Score Prediction, by Hui Wang et al.
Uncertainty-Aware Mean Opinion Score Prediction
by Hui Wang, Shiwan Zhao, Jiaming Zhou, Xiguang Zheng, Haoqin Sun, Xuechen Wang, Yong Qin
First submitted to arxiv on: 23 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Sound (cs.SD); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a novel approach to predicting Mean Opinion Score (MOS) by developing an uncertainty-aware MOS prediction system that can effectively handle aleatory and epistemic uncertainties using heteroscedastic regression and Monte Carlo dropout. The proposed method captures uncertainty well, enabling selective prediction and out-of-domain detection, thereby enhancing the practical utility of MOS systems in diverse real-world environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper aims to improve Mean Opinion Score (MOS) prediction by developing a system that can handle uncertainties. Currently, MOS prediction models struggle with performance instability across different samples. To address this issue, the authors identify and analyze sources of uncertainty, then propose an uncertainty-aware system that uses regression and dropout techniques. The results show that the new approach accurately predicts uncertainty and allows for selective predictions in unknown environments. |
Keywords
» Artificial intelligence » Dropout » Regression
