Spoken language understanding (SLU) is an essential task for machines to
understand human speech for better interactions. However, errors from the
automatic speech recognizer (ASR) usually hurt the understanding performance.
In reality, ASR systems may not be easy to adjust for the target scenarios.
Therefore, this paper focuses on learning utterance representations that are
robust to ASR errors using a contrastive objective, and further strengthens the
generalization ability by combining supervised contrastive learning and
self-distillation in model fine-tuning. Experiments on three benchmark datasets
demonstrate the effectiveness of our proposed approach.

本文提出了一种利用对比目标、监督对比学习和自蒸馏相结合的方法来学习对语音识别中的错误具有鲁棒性的话语表示，并进一步增强了其泛化能力。在三个基准数据集上的实验表明了我们提出的方法的有效性。

对比学习：提高口语理解的 ASR 鲁棒性

Contrastive Learning for Improving ASR Robustness in Spoken Language Understanding

Spoken Language Understanding (SLU) systems parse speech into semantic
structures like dialog acts and slots. This involves the use of an Automatic
Speech Recognizer (ASR) to transcribe speech into multiple text alternatives
(hypotheses). Transcription errors, common in ASRs, impact downstream SLU
performance negatively. Approaches to mitigate such errors involve using richer
information from the ASR, either in form of N-best hypotheses or word-lattices.
We hypothesize that transformer models learn better with a simpler utterance
representation using the concatenation of the N-best ASR alternatives, where
each alternative is separated by a special delimiter [SEP]. In our work, we
test our hypothesis by using concatenated N-best ASR alternatives as the input
to transformer encoder models, namely BERT and XLM-RoBERTa, and achieve
performance equivalent to the prior state-of-the-art model on DSTC2 dataset. We
also show that our approach significantly outperforms the prior
state-of-the-art when subjected to the low data regime. Additionally, this
methodology is accessible to users of third-party ASR APIs which do not provide
word-lattice information.

使用自动语音识别提供的多个文本替代方案，采用简化的表述方式，通过 transformer 模型 BERT 和 XLM-RoBERTa 对 N-best 替代方案的连接作为输人，提高了 Spoken Language Understanding 系统的性能。