首个考虑采样率的语音质量评估模型，提升高采样率音频预测准确性。
HighRateMOS: Sampling-Rate Aware Modeling for Speech Quality Assessment
- 引入可学习的采样率嵌入，融合自监督与多尺度特征
- 在AudioMOS 2025中五项指标排名第一，显著降低采样率偏差
- 适合需要跨采样率部署的语音质量评估场景
当前语音质量预测模型通常在固定采样率音频上训练，面对更高采样率测试数据时会产生偏差。本文提出HighRateMOS，首个非侵入式均值意见分（MOS）模型，显式建模采样率影响。该模型集成三种变体，利用：(i) 可学习的语音采样率嵌入，(ii) Wav2vec 2.0自监督嵌入，(iii) 多尺度CNN频谱特征，(iv) MFCC特征。在AudioMOS 2025 Track3中，HighRateMOS在八项指标中的五项排名第一。实验表明，直接建模采样率能实现更鲁棒、采样率无关的语音质量预测。
原文摘要 · Abstract (English)
Modern speech quality prediction models are trained on audio data resampled to a specific sampling rate. When faced with higher-rate audio at test time, these models can produce biased scores. We introduce HighRateMOS, the first non-intrusive mean opinion score (MOS) model that explicitly considers sampling rate. HighRateMOS ensembles three model variants that exploit the following information: (i) a learnable embedding of speech sampling rate, (ii) Wav2vec 2.0 self-supervised embeddings, (iii) multi-scale CNN spectral features, and (iv) MFCC features. In AudioMOS 2025 Track3, HighRateMOS ranked first in five out of eight metrics. Our experiments confirm that modeling the sampling rate directly leads to more robust and sampling-rate-agnostic speech quality predictions.
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