Language understanding in speech-based systems have attracted much attention
in recent years with the growing demand for voice interface applications.
However, the robustness of natural language understanding (NLU) systems to
errors introduced by automatic speech recognition (ASR) is under-examined. %To
facilitate the research on ASR-robust general language understanding, In this
paper, we propose ASR-GLUE benchmark, a new collection of 6 different NLU tasks
for evaluating the performance of models under ASR error across 3 different
levels of background noise and 6 speakers with various voice characteristics.
Based on the proposed benchmark, we systematically investigate the effect of
ASR error on NLU tasks in terms of noise intensity, error type and speaker
variants. We further purpose two ways, correction-based method and data
augmentation-based method to improve robustness of the NLU systems. Extensive
experimental results and analysises show that the proposed methods are
effective to some extent, but still far from human performance, demonstrating
that NLU under ASR error is still very challenging and requires further
research.

为了评估 ASR 误差下 NLU 系统的性能，本研究提出了 ASR-GLUE benchmark，包括 6 个不同的 NLU 任务，以及声音特点和噪声等级的变化。研究结果表明，基于数据增强和纠错的方法可以在一定程度上提高 NLU 系统的鲁棒性，但仍需要进一步研究。

ASR-GLUE：一个新的 ASR-Robust 自然语言理解多任务基准

ASR-GLUE: A New Multi-task Benchmark for ASR-Robust Natural Language  Understanding

We investigate the incremental stability properties of It\^o stochastic
dynamical systems. Specifically, we derive a stochastic version of nonlinear
contraction theory that provides a bound on the mean square distance between
any two trajectories of a stochastically contracting system. This bound can be
expressed as a function of the noise intensity and the contraction rate of the
noise-free system. We illustrate these results in the contexts of stochastic
nonlinear observers design and stochastic synchronization.

该论文研究了 Itô 随机动力系统的渐进稳定性，并提出了一种用于测量随机收缩系统中任意两条轨迹之间均方距离的随机非线性收缩理论。该理论可以被表达为无噪声系统的收缩速率和噪声强度的函数，并且在随机非线性观测器设计和随机同步化的背景下得到了应用。