This paper introduces NoRefER, a novel referenceless quality metric for automatic speech recognition (ASR) systems. Traditional reference-based metrics for evaluating ASR systems require costly ground-truth transcripts. NoRefER overcomes this limitation by fine-tuning a multilingual language model for pair-wise ranking ASR hypotheses using contrastive learning with Siamese network architecture. The self-supervised NoRefER exploits the known quality relationships between hypotheses from multiple compression levels of an ASR for learning to rank intra-sample hypotheses by quality, which is essential for model comparisons. The semi-supervised version also uses a referenced dataset to improve its inter-sample quality ranking, which is crucial for selecting potentially erroneous samples. The results indicate that NoRefER correlates highly with reference-based metrics and their intra-sample ranks, indicating a high potential for referenceless ASR evaluation or a/b testing.

翻译：本文提出了NoRefER，一种用于自动语音识别（ASR）系统的创新性无参考质量指标。传统的基于参考的ASR评估指标需要昂贵的地面真实转录文本。NoRefER通过使用暹罗网络架构进行对比学习，微调多语言语言模型以对ASR假设进行成对排序，克服了这一限制。自监督NoRefER利用ASR多个压缩级别假设之间的已知质量关系，学习按质量对样本内假设进行排序，这对模型比较至关重要。半监督版本还使用带参考的数据集改进样本间质量排序，这对于选择潜在错误样本尤为关键。结果表明，NoRefER与基于参考的指标及其样本内排序具有高度相关性，表明其在无参考ASR评估或A/B测试中具有巨大潜力。