用自监督学习提升多采样率语音自然度评分预测性能
Multi-Sampling-Frequency Naturalness MOS Prediction Using Self-Supervised Learning Model with Sampling-Frequency-Independent Layer
- 在自监督模型中加入采样率无关卷积层，提取通用语音特征
- 在AMC 2025挑战赛中，一项指标排名第一，总排名第四
- 适合语音质量评估、跨采样率应用的研究者参考
我们提交至AudioMOS挑战赛（AMC）2025 Track 3的方案，针对多采样频率（SFs）语音的平均意见分（MOS）预测任务。所提模型将采样率无关（SFI）卷积层嵌入自监督学习（SSL）框架，实现对不同采样率语音的统一特征提取。为提升预测性能，采用从预训练非SFI-SSL模型中迁移知识，并利用大规模MOS数据集进行预训练。在挑战赛中，该方法在一项评价指标上排名第一，最终排名第四。我们还报告了消融实验结果，验证了模型关键设计的有效性。
原文摘要 · Abstract (English)
We introduce our submission to the AudioMOS Challenge (AMC) 2025 Track 3: mean opinion score (MOS) prediction for speech with multiple sampling frequencies (SFs). Our submitted model integrates an SF-independent (SFI) convolutional layer into a self-supervised learning (SSL) model to achieve SFI speech feature extraction for MOS prediction. We present some strategies to improve the MOS prediction performance of our model: distilling knowledge from a pretrained non-SFI-SSL model and pretraining with a large-scale MOS dataset. Our submission to the AMC 2025 Track 3 ranked the first in one evaluation metric and the fourth in the final ranking. We also report the results of our ablation study to investigate essential factors of our model.
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