Intent classification is a fundamental task in the spoken language understanding field that has recently gained the attention of the scientific community, mainly because of the feasibility of approaching it with end-to-end neural models. In this way, avoiding using intermediate steps, i.e. automatic speech recognition, is possible, thus the propagation of errors due to background noise, spontaneous speech, speaking styles of users, etc. Towards the development of solutions applicable in real scenarios, it is interesting to investigate how environmental noise and related noise reduction techniques to address the intent classification task with end-to-end neural models. In this paper, we experiment with a noisy version of the fluent speech command data set, combining the intent classifier with a time-domain speech enhancement solution based on Wave-U-Net and considering different training strategies. Experimental results reveal that, for this task, the use of speech enhancement greatly improves the classification accuracy in noisy conditions, in particular when the classification model is trained on enhanced signals.

翻译：意图分类是口语理解领域中的基础任务，近期因其可通过端到端神经模型实现而受到科学界的广泛关注。这种方式可避免使用中间步骤（即自动语音识别），从而消除由背景噪声、自发语音、用户说话风格等因素导致的误差传播。为开发适用于实际场景的解决方案，有必要探究环境噪声及相关降噪技术如何与端到端神经模型相结合以完成意图分类任务。本文针对含噪声版本的Fluent Speech Commands数据集进行实验，将基于Wave-U-Net的时域语音增强方案与意图分类器相结合，并考虑不同的训练策略。实验结果表明，在该任务中，使用语音增强技术可显著提升噪声环境下的分类准确率，尤其是当分类模型在增强信号上进行训练时效果尤为突出。