NoRefER：一种通过半监督语言模型微调与对比学习实现的无参考自动语音识别质量度量
NoRefER: a Referenceless Quality Metric for Automatic Speech Recognition via Semi-Supervised Language Model Fine-Tuning with Contrastive Learning
AI总结：
本文提出无参考ASR质量度量NoRefER，通过对比学习和孪生网络微调多语言模型，实现无需真实转录的假设排序，与参考度量高度相关，适用于ASR评估和A/B测试。
摘要：
本文介绍了NoRefER，一种用于自动语音识别（ASR）系统的新型无参考质量度量。传统的基于参考的ASR系统评估度量需要昂贵的真实转录文本。NoRefER通过使用对比学习和孪生网络架构微调多语言语言模型，以进行ASR假设的成对排序，从而克服了这一限制。自监督的NoRefER利用ASR多个压缩级别假设之间已知的质量关系，学习按质量对样本内假设进行排序，这对于模型比较至关重要。半监督版本还使用带参考的数据集来改进其样本间质量排序，这对于选择可能出错的样本至关重要。结果表明，NoRefER与基于参考的度量及其样本内排名高度相关，表明其在无参考ASR评估或A/B测试方面具有巨大潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
