The common standard for quality evaluation of automatic speech recognition (ASR) systems is reference-based metrics such as the Word Error Rate (WER), computed using manual ground-truth transcriptions that are time-consuming and expensive to obtain. This work proposes a multi-language referenceless quality metric, which allows comparing the performance of different ASR models on a speech dataset without ground truth transcriptions. To estimate the quality of ASR hypotheses, a pre-trained language model (LM) is fine-tuned with contrastive learning in a self-supervised learning manner. In experiments conducted on several unseen test datasets consisting of outputs from top commercial ASR engines in various languages, the proposed referenceless metric obtains a much higher correlation with WER scores and their ranks than the perplexity metric from the state-of-art multi-lingual LM in all experiments, and also reduces WER by more than $7\%$ when used for ensembling hypotheses. The fine-tuned model and experiments are made available for the reproducibility: https://github.com/aixplain/NoRefER

本文提出了一种多语言无参考质量度量方法，使用预先训练的语言模型采用对比学习的自我监督学习方式进行微调，针对自动语音识别模型提出了一个评估标准，该评估标准可以在没有参考标记的情况下比较不同自动语音识别模型的性能，并在减少识别错误和提高性能方面效果显著。

基于多语言自监督对比学习的自动语音识别无参考质量度量