This paper addresses the problem of automatic speech recognition (ASR) error
detection and their use for improving spoken language understanding (SLU)
systems. In this study, the SLU task consists in automatically extracting, from
ASR transcriptions , semantic concepts and concept/values pairs in a e.g
touristic information system. An approach is proposed for enriching the set of
semantic labels with error specific labels and by using a recently proposed
neural approach based on word embeddings to compute well calibrated ASR
confidence measures. Experimental results are reported showing that it is
possible to decrease significantly the Concept/Value Error Rate with a state of
the art system, outperforming previously published results performance on the
same experimental data. It also shown that combining an SLU approach based on
conditional random fields with a neural encoder/decoder attention based
architecture , it is possible to effectively identifying confidence islands and
uncertain semantic output segments useful for deciding appropriate error
handling actions by the dialogue manager strategy .

介绍了一种基于神经网络的自然语言处理方法，用于识别语音识别中的错误和改进语音理解系统的性能。实验结果表明，该方案可以显著降低概念 / 值错误率，并且结合基于条件随机场的 SLU 方法和神经编码器 / 解码器注意力机制，可以识别出置信度区间和不确定性的语义输出片段，进而决定适当的错误处理措施。

提高口语理解的 ASR 错误管理

ASR error management for improving spoken language understanding

In this paper, we present the first experiments using neural network models
for the task of error detection in learner writing. We perform a systematic
comparison of alternative compositional architectures and propose a framework
for error detection based on bidirectional LSTMs. Experiments on the CoNLL-14
shared task dataset show the model is able to outperform other participants on
detecting errors in learner writing. Finally, the model is integrated with a
publicly deployed self-assessment system, leading to performance comparable to
human annotators.

本文介绍使用神经网络模型进行学习者写作错误检测的实验。在 CoNLL-14 共享任务数据集上，使用双向 LSTMs 框架的模型表现优于其他参与者。最终，在公开部署的自我评估系统中集成该模型，其性能与人类注释者相当。