对比45种语音质量评估指标，发现神经网络类指标在干净语音下更可靠。
Assessing speech quality metrics for evaluation of neural audio codecs under clean speech conditions
- 在17种编码器条件下，用主观评分验证45个客观指标
- scoreq和utmos等神经指标与主观评分相关性最高
- 非侵入式指标在高质量时易饱和，不敏感
客观语音质量度量被广泛用于评估编解码器性能。然而，对于神经编解码器，哪些度量能提供可靠的品质估计仍不明确。为此，我们通过将45种客观度量的得分与17种编码条件下的主观听觉评分进行相关性分析，评估其表现。结果显示，基于神经网络的度量如scoreq和utmos在与主观评分的相关性上表现最佳。进一步分析不同主观质量区间的性能发现，非侵入式度量在高主观质量水平下容易饱和。
原文摘要 · Abstract (English)
Objective speech-quality metrics are widely used to assess codec performance. However, for neural codecs, it is often unclear which metrics provide reliable quality estimates. To address this, we evaluated 45 objective metrics by correlating their scores with subjective listening scores for clean speech across 17 codec conditions. Neural-based metrics such as scoreq and utmos achieved the highest Pearson correlations with subjective scores. Further analysis across different subjective quality ranges revealed that non-intrusive metrics tend to saturate at high subjective quality levels.
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