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LLMs can effectively aggregate diverse speech quality metrics, even outperforming specialized models when labeled data is scarce.
Speech quality assessment is skewed: male listeners consistently give higher scores than female listeners, and standard MOS models learn and perpetuate this bias.
Time-shifted anechoic speech beats early reflections as a training target for universal speech enhancement, leading to better perceptual quality and ASR performance.
